Displaying publications 1 - 20 of 309 in total

Abstract:
Sort:
  1. Md Noh MF, Gunasegavan RD, Mustafa Khalid N, Balasubramaniam V, Mustar S, Abd Rashed A
    Molecules, 2020 Oct 06;25(19).
    PMID: 33036314 DOI: 10.3390/molecules25194567
    Food composition database (FCD) provides the nutritional composition of foods. Reliable and up-to date FCD is important in many aspects of nutrition, dietetics, health, food science, biodiversity, plant breeding, food industry, trade and food regulation. FCD has been used extensively in nutrition labelling, nutritional analysis, research, regulation, national food and nutrition policy. The choice of method for the analysis of samples for FCD often depends on detection capability, along with ease of use, speed of analysis and low cost. Sample preparation is the most critical stage in analytical method development. Samples can be prepared using numerous techniques; however it should be applicable for a wide range of analytes and sample matrices. There are quite a number of significant improvements on sample preparation techniques in various food matrices for specific analytes highlighted in the literatures. Improvements on the technology used for the analysis of samples by specific instrumentation could provide an alternative to the analyst to choose for their laboratory requirement. This review provides the reader with an overview of recent techniques that can be used for sample preparation and instrumentation for food analysis which can provide wide options to the analysts in providing data to their FCD.
    Matched MeSH terms: Databases, Factual*
  2. Ramanjot, Mittal U, Wadhawan A, Singla J, Jhanjhi NZ, Ghoniem RM, et al.
    Sensors (Basel), 2023 May 15;23(10).
    PMID: 37430683 DOI: 10.3390/s23104769
    A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.
    Matched MeSH terms: Databases, Factual
  3. Ahmed A, Saeed F, Salim N, Abdo A
    J Cheminform, 2014;6:19.
    PMID: 24883114 DOI: 10.1186/1758-2946-6-19
    BACKGROUND: It is known that any individual similarity measure will not always give the best recall of active molecule structure for all types of activity classes. Recently, the effectiveness of ligand-based virtual screening approaches can be enhanced by using data fusion. Data fusion can be implemented using two different approaches: group fusion and similarity fusion. Similarity fusion involves searching using multiple similarity measures. The similarity scores, or ranking, for each similarity measure are combined to obtain the final ranking of the compounds in the database.

    RESULTS: The Condorcet fusion method was examined. This approach combines the outputs of similarity searches from eleven association and distance similarity coefficients, and then the winner measure for each class of molecules, based on Condorcet fusion, was chosen to be the best method of searching. The recall of retrieved active molecules at top 5% and significant test are used to evaluate our proposed method. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints.

    CONCLUSIONS: Simulated virtual screening experiments with the standard two data sets show that the use of Condorcet fusion provides a very simple way of improving the ligand-based virtual screening, especially when the active molecules being sought have a lowest degree of structural heterogeneity. However, the effectiveness of the Condorcet fusion was increased slightly when structural sets of high diversity activities were being sought.

    Matched MeSH terms: Databases, Factual
  4. Abdul Hamid M
    Med J Malaysia, 2008 Sep;63 Suppl C:vii.
    PMID: 19227668
    Matched MeSH terms: Databases, Factual*
  5. Lee FY, Wong HS, Chan HK, Mohamed Ali N, Abu Hassan MR, Omar H, et al.
    Pharmacoepidemiol Drug Saf, 2020 12;29(12):1669-1679.
    PMID: 33064335 DOI: 10.1002/pds.5153
    PURPOSE: To determine the incidence, demographic profile, background of reporters, causative agents, severity and clinical outcomes of hepatic adverse drug reaction (ADR) reports in Malaysia using the national ADR reporting database.

    METHODS: The ADR reports recorded between 2000 and 2017 were retrospectively analysed to identify hepatic ADR reports. The trend and characteristics of hepatic ADR cases were described. Multivariate disproportionality analysis of the causative agents was performed to generate signals of hepatic ADRs.

    RESULTS: A total of 2090 hepatic ADRs (1.77% of all ADRs) were reported with mortality rate of 12.7% among cases with known clinical outcomes. The incidence of hepatic ADR reporting in Malaysia increased significantly over 18 years from 0.26 to 9.45 per million population (P 

    Matched MeSH terms: Databases, Factual
  6. Ng KT, Lim WE, Teoh WY, Shariffuddin II, Ti LK, Abidin MFBZ
    J Anesth, 2024 Feb;38(1):65-76.
    PMID: 38019351 DOI: 10.1007/s00540-023-03281-6
    PURPOSE: Midline approach of spinal anesthesia has been widely used for patients undergoing surgical procedures. However, it might not be effective for obstetric patients and elderly with degenerative spine changes. Primary objective was to examine the success rate at the first attempt between the paramedian and midline spinal anesthesia in adults undergoing surgery.

    METHODS: Databases of MEDLINE, EMBASE, and CENTRAL were searched from their starting date until February 2023. Randomized clinical trials (RCTs) comparing the paramedian versus midline approach of spinal anesthesia were included. The primary outcome was the success rate at the first attempt of spinal anesthesia.

    RESULTS: Our review included 36 RCTs (n = 5379). Compared to the midline approach, paramedian approach may increase success rate at the first attempt but the evidence is very uncertain (OR: 0.47, 95% CI 0.27-0.82, ρ = 0.007, level of evidence:very low). Our pooled data indicates that the paramedian approach likely reduced incidence of post-spinal headache (OR: 2.07, 95% CI 1.51-2.84, ρ 

    Matched MeSH terms: Databases, Factual
  7. Yildirim O, Talo M, Ay B, Baloglu UB, Aydin G, Acharya UR
    Comput Biol Med, 2019 10;113:103387.
    PMID: 31421276 DOI: 10.1016/j.compbiomed.2019.103387
    In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significantly to improvement in the quality of healthcare. In order for deep learning models to perform well, large datasets are required for training. However, a difficulty in the biomedical field is the lack of clinical data with expert annotation. A recent, commonly implemented technique to train deep learning models using small datasets is to transfer the weighting, developed from a large dataset, to the current model. This deep learning transfer strategy is generally employed for two-dimensional signals. Herein, the weighting of models pre-trained using two-dimensional large image data was applied to one-dimensional HR signals. The one-dimensional HR signals were then converted into frequency spectrum images, which were utilized for application to well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet. The DenseNet pre-trained model yielded the highest classification average accuracy of 97.62%, and sensitivity of 100%, to detect DM subjects via HR signal recordings. In the future, we intend to further test this developed model by utilizing additional data along with cloud-based storage to diagnose DM via heart signal analysis.
    Matched MeSH terms: Databases, Factual*
  8. Sharma M, Goyal D, Achuth PV, Acharya UR
    Comput Biol Med, 2018 07 01;98:58-75.
    PMID: 29775912 DOI: 10.1016/j.compbiomed.2018.04.025
    Sleep related disorder causes diminished quality of lives in human beings. Sleep scoring or sleep staging is the process of classifying various sleep stages which helps to detect the quality of sleep. The identification of sleep-stages using electroencephalogram (EEG) signals is an arduous task. Just by looking at an EEG signal, one cannot determine the sleep stages precisely. Sleep specialists may make errors in identifying sleep stages by visual inspection. To mitigate the erroneous identification and to reduce the burden on doctors, a computer-aided EEG based system can be deployed in the hospitals, which can help identify the sleep stages, correctly. Several automated systems based on the analysis of polysomnographic (PSG) signals have been proposed. A few sleep stage scoring systems using EEG signals have also been proposed. But, still there is a need for a robust and accurate portable system developed using huge dataset. In this study, we have developed a new single-channel EEG based sleep-stages identification system using a novel set of wavelet-based features extracted from a large EEG dataset. We employed a novel three-band time-frequency localized (TBTFL) wavelet filter bank (FB). The EEG signals are decomposed using three-level wavelet decomposition, yielding seven sub-bands (SBs). This is followed by the computation of discriminating features namely, log-energy (LE), signal-fractal-dimensions (SFD), and signal-sample-entropy (SSE) from all seven SBs. The extracted features are ranked and fed to the support vector machine (SVM) and other supervised learning classifiers. In this study, we have considered five different classification problems (CPs), (two-class (CP-1), three-class (CP-2), four-class (CP-3), five-class (CP-4) and six-class (CP-5)). The proposed system yielded accuracies of 98.3%, 93.9%, 92.1%, 91.7%, and 91.5% for CP-1 to CP-5, respectively, using 10-fold cross validation (CV) technique.
    Matched MeSH terms: Databases, Factual
  9. Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR
    Comput Methods Programs Biomed, 2019 Jul;176:121-133.
    PMID: 31200900 DOI: 10.1016/j.cmpb.2019.05.004
    BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.

    METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.

    RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.

    CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.

    Matched MeSH terms: Databases, Factual
  10. Alizadehsani R, Abdar M, Roshanzamir M, Khosravi A, Kebria PM, Khozeimeh F, et al.
    Comput Biol Med, 2019 08;111:103346.
    PMID: 31288140 DOI: 10.1016/j.compbiomed.2019.103346
    Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is the standard procedure for diagnosing CAD. Alternatively, machine learning (ML) techniques have been widely used in the literature as fast, affordable, and noninvasive approaches for CAD detection. The results that have been published on ML-based CAD diagnosis differ substantially in terms of the analyzed datasets, sample sizes, features, location of data collection, performance metrics, and applied ML techniques. Due to these fundamental differences, achievements in the literature cannot be generalized. This paper conducts a comprehensive and multifaceted review of all relevant studies that were published between 1992 and 2019 for ML-based CAD diagnosis. The impacts of various factors, such as dataset characteristics (geographical location, sample size, features, and the stenosis of each coronary artery) and applied ML techniques (feature selection, performance metrics, and method) are investigated in detail. Finally, the important challenges and shortcomings of ML-based CAD diagnosis are discussed.
    Matched MeSH terms: Databases, Factual
  11. Raghavendra U, Gudigar A, Maithri M, Gertych A, Meiburger KM, Yeong CH, et al.
    Comput Biol Med, 2018 04 01;95:55-62.
    PMID: 29455080 DOI: 10.1016/j.compbiomed.2018.02.002
    Ultrasound imaging is one of the most common visualizing tools used by radiologists to identify the location of thyroid nodules. However, visual assessment of nodules is difficult and often affected by inter- and intra-observer variabilities. Thus, a computer-aided diagnosis (CAD) system can be helpful to cross-verify the severity of nodules. This paper proposes a new CAD system to characterize thyroid nodules using optimized multi-level elongated quinary patterns. In this study, higher order spectral (HOS) entropy features extracted from these patterns appropriately distinguished benign and malignant nodules under particle swarm optimization (PSO) and support vector machine (SVM) frameworks. Our CAD algorithm achieved a maximum accuracy of 97.71% and 97.01% in private and public datasets respectively. The evaluation of this CAD system on both private and public datasets confirmed its effectiveness as a secondary tool in assisting radiological findings.
    Matched MeSH terms: Databases, Factual*
  12. Barua PD, Baygin N, Dogan S, Baygin M, Arunkumar N, Fujita H, et al.
    Sci Rep, 2022 Oct 14;12(1):17297.
    PMID: 36241674 DOI: 10.1038/s41598-022-21380-4
    Pain intensity classification using facial images is a challenging problem in computer vision research. This work proposed a patch and transfer learning-based model to classify various pain intensities using facial images. The input facial images were segmented into dynamic-sized horizontal patches or "shutter blinds". A lightweight deep network DarkNet19 pre-trained on ImageNet1K was used to generate deep features from the shutter blinds and the undivided resized segmented input facial image. The most discriminative features were selected from these deep features using iterative neighborhood component analysis, which were then fed to a standard shallow fine k-nearest neighbor classifier for classification using tenfold cross-validation. The proposed shutter blinds-based model was trained and tested on datasets derived from two public databases-University of Northern British Columbia-McMaster Shoulder Pain Expression Archive Database and Denver Intensity of Spontaneous Facial Action Database-which both comprised four pain intensity classes that had been labeled by human experts using validated facial action coding system methodology. Our shutter blinds-based classification model attained more than 95% overall accuracy rates on both datasets. The excellent performance suggests that the automated pain intensity classification model can be deployed to assist doctors in the non-verbal detection of pain using facial images in various situations (e.g., non-communicative patients or during surgery). This system can facilitate timely detection and management of pain.
    Matched MeSH terms: Databases, Factual
  13. Xu S, Deo RC, Soar J, Barua PD, Faust O, Homaira N, et al.
    Comput Methods Programs Biomed, 2023 Nov;241:107746.
    PMID: 37660550 DOI: 10.1016/j.cmpb.2023.107746
    BACKGROUND AND OBJECTIVE: Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality.

    METHODS: We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance.

    RESULTS: We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent.

    CONCLUSIONS: Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.

    Matched MeSH terms: Databases, Factual
  14. Fuziah MZ, Hong JY, Zanariah H, Harun F, Chan SP, Rokiah P, et al.
    Med J Malaysia, 2008 Sep;63 Suppl C:37-40.
    PMID: 19230245
    In Malaysia, Diabetes in Children and Adolescents Registry (DiCARE) was launched nationwide in August 2006 to determine and monitor the number, the time trend of diabetes mellitus (DM) patients, their socio-demographic profiles, outcome of intervention and facilitate research using this registry. This is an on going real time register of diabetic patients < or = 20 years old via the e-DiCARE, an online registration system. To date were 240 patients notified from various states in Malaysia. The mean age was 12.51 years (1.08-19.75) and 46.4% were boys. The mean age at diagnosis was 8.31 +/- 4.13 years old with an estimated duration of diabetes of 4.32 +/- 3.55 years. A total of 166/240 (69.2%) have T1DM, 42/240 (17.5%) have T2DM and 18/240 (7.5%) have other types of DM. Basis of diagnosis was known in 162 patients with T1DM and 41 patients with T2DM. In T1DM patients, 6.0% of the girls and 19.1% boys were overweight or obese. As for T2DM, 64.3% had their BMI reported: 66.7% girls and 91.6% boys were overweight or obese. Most patients (80.4%) practiced home blood glucose monitoring. Patients were seen by dietitian (66.7%), diabetes educator (50.0%), and optometrist or ophthalmologist (45.0%). Only 10.8% attended diabetic camps. In the annual census of 117 patients, the mean HbAlc level was 10.0% + 2.2 (range 5.2 to 17.0%). The early results of DiCARE served as a starting point to improve the standard of care of DM among the young in the country.
    Matched MeSH terms: Databases, Factual
  15. Muhammad Zubir Yusof, Nik Ahmad Kamal Nik Mahmod, Nor Azlina A. Rahman, Ailin Razali, Niza Samsuddin, Nik Mohamed Nizan Nik Mohamed, et al.
    MyJurnal
    Occupational diseases are one of the major health problems related to workplace hazards.
    However, the epidemiological data for this problem is scarce especially among Small and
    Medium Industry (SMI) workers. These workers are vulnerable to occupational health problem
    due to lack of knowledge and implementation of health and safety in the workplace. In Malaysia,
    most of the SMI workers have limited coverage for basic occupational health services which
    may worsen their health. Thus, this article aims to provide a review on the burden of
    occupational health problems among them. The electronic and library searches were used to
    extract the information from both published and unpublished articles that were not limited to any
    year of publication until 2017. One hundred and ninety-six published articles and 198
    unpublished articles were retrieved from the database. Only 19 published articles and 25
    unpublished articles met the eligibility criteria. Prevalence data of occupational
    diseases/poisoning, including overall and body specific (musculoskeletal disorders) was
    extracted in raw data from the eligible studies. Prevalent statistics on occupational
    musculoskeletal diseases (1.3% - 97.6%), noise-induced hearing loss (29.4% - 73.3%),
    occupational skin diseases (10.5% - 84.3%), respiratory (1.9% - 92.2%) and occupational
    poisoning (14.9% - 17.7%) among the working population is different within published papers
    compared to unpublished ones. In Malaysia, there are no specific statistic that give a true picture
    of the burden of occupational diseases in the SMI. However, this review concludes that
    musculoskeletal diseases are significant occupational problems among SMI workers.
    Matched MeSH terms: Databases, Factual
  16. Alogaili F, Abdul Ghani N, Ahmad Kharman Shah N
    J Infect Public Health, 2020 Oct;13(10):1456-1461.
    PMID: 32694082 DOI: 10.1016/j.jiph.2020.06.035
    Prescription Drug Monitoring Program (PDMP) is an electronic database that tracks the prescriptions of controlled drugs with its aims to combat the incidence of drug abuse. Although the establishment of PDMP in the US was since 2003, evidence of the impact of PDMP's strength and weakness towards its implementation is still scarce. A systematic literature review according to Preferred Reporting Items for Systematic Review (PRISMA) standard was conducted to investigate the influence of PDMP's strength in combating the incidence of drug abuse and also to review the weaknesses of PDMP that prohibit its implementation. Results from this study reveal that the implementation of PDMP has mitigated the issue of drug abuse and has increased work efficiency among healthcare practitioners. However, the implementation rate of this system is low due to its weaknesses such as limited internet access and limited access to the PDMP system. Therefore, efforts to overcome the weaknesses of PDMP need to be instituted to ensure the healthcare system could fully optimize PDMP's benefits.
    Matched MeSH terms: Databases, Factual
  17. Lazim N, Elias MH, Sutaji Z, Abdul Karim AK, Abu MA, Ugusman A, et al.
    Int J Mol Sci, 2023 Aug 17;24(16).
    PMID: 37629050 DOI: 10.3390/ijms241612869
    The homeobox A10 (HOXA10) gene is known to be related to endometriosis; however, due to a lack of knowledge/evidence in the pathogenesis of endometriosis, the mechanisms that link HOXA10 to endometriosis still need to be clarified. This review addresses the difference in the expression of the HOXA10 gene in endometriotic women versus non-endometriotic women across populations by country and discusses its influences on women's fertility. An organized search of electronic databases was conducted in Scopus, ScienceDirect, PubMed, and Web of Science. The keywords used were (HOXA10 OR "homeobox A10" OR PL OR HOX1 OR HOX1H OR HOX1.8) AND ("gene expression") AND (endometriosis). The initial search resulted in 623 articles, 10 of which were included in this review. All ten papers included in this study were rated fair in terms of the quality of the studies conducted. The expression of the HOXA10 gene was found to be downregulated in most studies. However, one study provided evidence of the downregulation and upregulation of HOXA10 gene expression due to the localization of endometriotic lesions. Measuring the expression of the HOXA10 gene in women is clinically essential to predicting endometriosis, endometrial receptivity, and the development of pinopodes in the endometrium during the luteal phase.
    Matched MeSH terms: Databases, Factual
  18. Zin CS, Taufek NH, Ahmad MM
    Front Pharmacol, 2019;10:1286.
    PMID: 31736760 DOI: 10.3389/fphar.2019.01286
    Limited data are available on the adherence to opioid therapy and the influence of different patient groups on adherence. This study examined the patterns of adherence in opioid naïve and opioid existing patients with varying age and gender. This retrospective cohort study was conducted using the prescription databases in tertiary hospital settings in Malaysia from 2010 to 2016. Adult patients aged ≥18 years, receiving at least two opioid prescriptions, were included and stratified into the opioid naïve and existing patient groups. Adherence to opioid therapy was measured using the proportion of days covered (PDC), which was derived by dividing the total number of days covered with any opioids by the number of days in the follow-up period. Generalized linear modeling was used to assess factors associated with PDC. A total of 10,569 patients with 36,650 prescription episodes were included in the study. Of these, 91.7% (n = 9,696) were opioid naïve patients and 8.3% (n = 873) were opioid existing patients. The median PDC was 35.5% (interquartile range (IQR) 10.3-78.7%) and 26.8% (IQR 8.8-69.5%) for opioid naïve and opioid existing patients, respectively. A higher opioid daily dose (coefficient 0.010, confidence interval (CI) 0.009, 0.012 p < 0.0001) and increasing age (coefficient 0.002, CI 0.001, 0.003 p < 0.0001) were associated with higher levels of PDC, while lower PDC values were associated with male subjects (coefficient -0.0041, CI -0.072, -0.010 p = 0.009) and existing opioid patients (coefficient -0.134, CI -0.191, -0.077 p < 0.0001). The suboptimal adherence to opioid medications was commonly observed among patients with non-cancer pain, and the opioid existing patients were less adherent compared to opioid naïve patients. Increasing age and a higher daily opioid dose were factors associated with higher levels of adherence, while male and opioid existing patients were potential determinants for lower levels of adherence to opioid medications.
    Matched MeSH terms: Databases, Factual
  19. Xu T, Nordin NA, Aini AM
    Int J Environ Res Public Health, 2022 Oct 31;19(21).
    PMID: 36361106 DOI: 10.3390/ijerph192114227
    A growing number of articles have identified and reported the benefits and importance of urban green spaces for improving human well-being, but there is a significant knowledge gap regarding the impact of urban green spaces on the subjective well-being of older adults. The literature search (August 2015-August 2022) was derived from two major scientific databases, Google Scholar, and Web of Science. As a result, 2558 articles were found, 1527 of which were retrieved from WOS and the rest from Google Scholar. Bibliometric methods and VOSviewer software were used to screen and organize the articles in the relevant fields. Finally, 65 articles met the review criteria. The included studies aim to capture the benefits of various features of urban green spaces in meeting or enhancing the subjective well-being needs of older adults. The results of our review further support the existence of a strong link between older adults' subjective well-being and various features of urban green spaces, providing new insights for future in-depth reexamination and policy development. Furthermore, the relationship between urban green spaces and older adults' subjective well-being depends not only on the urban green spaces themselves but also on the characteristics of the older adult population that uses them.
    Matched MeSH terms: Databases, Factual
  20. Hannan MA, Arebey M, Begum RA, Basri H, Al Mamun MA
    Waste Manag, 2016 Apr;50:10-9.
    PMID: 26868844 DOI: 10.1016/j.wasman.2016.01.046
    This paper presents a CBIR system to investigate the use of image retrieval with an extracted texture from the image of a bin to detect the bin level. Various similarity distances like Euclidean, Bhattacharyya, Chi-squared, Cosine, and EMD are used with the CBIR system for calculating and comparing the distance between a query image and the images in a database to obtain the highest performance. In this study, the performance metrics is based on two quantitative evaluation criteria. The first one is the average retrieval rate based on the precision-recall graph and the second is the use of F1 measure which is the weighted harmonic mean of precision and recall. In case of feature extraction, texture is used as an image feature for bin level detection system. Various experiments are conducted with different features extraction techniques like Gabor wavelet filter, gray level co-occurrence matrix (GLCM), and gray level aura matrix (GLAM) to identify the level of the bin and its surrounding area. Intensive tests are conducted among 250bin images to assess the accuracy of the proposed feature extraction techniques. The average retrieval rate is used to evaluate the performance of the retrieval system. The result shows that, the EMD distance achieved high accuracy and provides better performance than the other distances.
    Matched MeSH terms: Databases, Factual
Filters
Contact Us

Please provide feedback to Administrator (afdal@afpm.org.my)

External Links