Displaying publications 21 - 40 of 312 in total

Abstract:
Sort:
  1. AlDahas A, Heneghan NR, Althobaiti S, Deane JA, Rushton A, Falla D
    BMC Musculoskelet Disord, 2024 Jan 10;25(1):44.
    PMID: 38200520 DOI: 10.1186/s12891-023-07111-4
    INTRODUCTION: Proprioception can be impaired in people with neck pain. The cervical joint position sense test, which measures joint position error (JPE), is the most common test used to assess neck proprioception. The aim of this systematic review was to assess the measurement properties of this test for the assessment of people with and without neck pain.

    METHODS: This systematic review was registered prospectively on Prospero (CRD42020188715). It was designed using the COSMIN guidelines and reported in line with the PRISMA checklist. Two reviewers independently searched Medline, Embase, SportDiscus, and CINAHL Plus databases from inception to the 24th July 2022 with an update of the search conducted until 14th of October 2023. The COSMIN risk of bias checklist was used to assess the risk of bias in each study. The updated criteria for good measurement properties were used to rate individual studies and then the overall pooled results. The level of evidence was rated by two reviewers independently using a modified GRADE approach.

    RESULTS: Fifteen studies were included in this review, 13 reporting absolute JPE and 2 reporting constant JPE. The measurement properties assessed were reliability, measurement error, and validity. The measurement of JPE showed sufficient reliability and validity, however, the level of evidence was low/very low for both measurement properties, apart from convergent validity of the constant JPE, which was high.

    CONCLUSION: The measure of cervical JPE showed sufficient reliability and validity but with low/very low levels of evidence. Further studies are required to investigate the reliability and validity of this test as well as the responsiveness of the measure.

    Matched MeSH terms: Databases, Factual
  2. Albahri OS, Al-Obaidi JR, Zaidan AA, Albahri AS, Zaidan BB, Salih MM, et al.
    Comput Methods Programs Biomed, 2020 Nov;196:105617.
    PMID: 32593060 DOI: 10.1016/j.cmpb.2020.105617
    CONTEXT: People who have recently recovered from the threat of deteriorating coronavirus disease-2019 (COVID-19) have antibodies to the coronavirus circulating in their blood. Thus, the transfusion of these antibodies to deteriorating patients could theoretically help boost their immune system. Biologically, two challenges need to be surmounted to allow convalescent plasma (CP) transfusion to rescue the most severe COVID-19 patients. First, convalescent subjects must meet donor selection plasma criteria and comply with national health requirements and known standard routine procedures. Second, multi-criteria decision-making (MCDM) problems should be considered in the selection of the most suitable CP and the prioritisation of patients with COVID-19.

    OBJECTIVE: This paper presents a rescue framework for the transfusion of the best CP to the most critical patients with COVID-19 on the basis of biological requirements by using machine learning and novel MCDM methods.

    METHOD: The proposed framework is illustrated on the basis of two distinct and consecutive phases (i.e. testing and development). In testing, ABO compatibility is assessed after classifying donors into the four blood types, namely, A, B, AB and O, to indicate the suitability and safety of plasma for administration in order to refine the CP tested list repository. The development phase includes patient and donor sides. In the patient side, prioritisation is performed using a contracted patient decision matrix constructed between 'serological/protein biomarkers and the ratio of the partial pressure of oxygen in arterial blood to fractional inspired oxygen criteria' and 'patient list based on novel MCDM method known as subjective and objective decision by opinion score method'. Then, the patients with the most urgent need are classified into the four blood types and matched with a tested CP list from the test phase in the donor side. Thereafter, the prioritisation of CP tested list is performed using the contracted CP decision matrix.

    RESULT: An intelligence-integrated concept is proposed to identify the most appropriate CP for corresponding prioritised patients with COVID-19 to help doctors hasten treatments.

    DISCUSSION: The proposed framework implies the benefits of providing effective care and prevention of the extremely rapidly spreading COVID-19 from affecting patients and the medical sector.

    Matched MeSH terms: Databases, Factual
  3. Alefishat E, Abu Farha R, Zawiah M
    PLoS One, 2021;16(8):e0256031.
    PMID: 34388191 DOI: 10.1371/journal.pone.0256031
    PURPOSE: The credibility and the reliability of Internet webpages to seek medication-related information is questionable. The main objective of the current study was to evaluate perception and experience of pharmacists with the use of Internet-based medication information by their patients.

    METHODS: This is a cross-sectional descriptive study that was conducted to evaluate perception and experience of pharmacists with the use of Internet-based medication information by their patients. During the study period, 200 pharmacists were approached to participate in the study using a paper-based survey to assess their perceptions and current experience with the use of Internet-based medication information by their patients. Data were analyzed using descriptive statistics (mean/standard deviation for continuous variables, and frequency/percentages for qualitative variables). Also, simple linear regression was utilized to screen factors affecting pharmacists' perception scores of the use of Internet-based medication information.

    RESULTS: Among 161 recruited pharmacists, the majority (n = 129, 80.1%) reported receiving inquiries from patients about Internet-based medication information within the last year. Among them, only 22.6% (n = 29) of pharmacists believed that Internet-based medication information is somewhat or very accurate. Unfortunately, only 24.2% (n = 31) of them stated that they always had enough time for their patient to discuss their Internet-based medication information. Regarding pharmacists' perception of the use of Internet-based medication information by their patients, more than half of the pharmacists (>50%) believe that Internet-based medication information could increase the patient's role in taking responsibility. On the other hand, 54.7% (n = 88) of the pharmacists believed that Internet-based medication information would contribute to rising the healthcare cost by obtaining unnecessary medications by patients. Finally, pharmacists' educational level was found to significantly affect their perception scores toward patient use of Internet-based medication information where those with higher educational level showed lower perception score (r = -0.200, P-value = 0.011).

    CONCLUSION: Although pharmacists felt that usage of Internet-based data by patients is beneficial, they also have believed that it has a negative impact in terms of rising the healthcare cost, and it promotes unnecessary fear or concern about medications. We suggest that pharmacists be trained on principles of critical appraisal to become professional in retrieval information on the Internet that might improve their delivery of healthcare information and their recommendations to patients.

    Matched MeSH terms: Databases, Factual
  4. Alhajj MN, Halboub E, Yacob N, Al-Maweri SA, Ahmad SF, Celebić A, et al.
    BMC Oral Health, 2024 Mar 04;24(1):303.
    PMID: 38439020 DOI: 10.1186/s12903-024-04083-2
    BACKGROUND: The present systematic review and meta-analysis investigated the available evidence about the adherence of Candida Albicans to the digitally-fabricated acrylic resins (both milled and 3D-printed) compared to the conventional heat-polymerized acrylic resins.

    METHODS: This study followed the guidelines of the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA). A comprehensive search of online databases/search tools (Web of Science, Scopus, PubMed, Ovid, and Google Scholar) was conducted for all relevant studies published up until May 29, 2023. Only in-vitro studies comparing the adherence of Candida albicans to the digital and conventional acrylic resins were included. The quantitative analyses were performed using RevMan v5.3 software.

    RESULTS: Fourteen studies were included, 11 of which were meta-analyzed based on Colony Forming Unit (CFU) and Optical Density (OD) outcome measures. The pooled data revealed significantly lower candida colonization on the milled digitally-fabricated compared to the heat-polymerized conventionally-fabricated acrylic resin materials (MD = - 0.36; 95%CI = - 0.69, - 0.03; P = 0.03 and MD = - 0.04; 95%CI = - 0.06, - 0.01; P = 0.0008; as measured by CFU and OD respectively). However, no differences were found in the adhesion of Candida albicans between the 3D-printed digitally-fabricated compared to the heat-polymerized conventionally-fabricated acrylic resin materials (CFU: P = 0.11, and OD: P = 0.20).

    CONCLUSION: The available evidence suggests that candida is less likely to adhere to the milled digitally-fabricated acrylic resins compared to the conventional ones.

    Matched MeSH terms: Databases, Factual
  5. Alhammadi MS, Halboub E, Fayed MS, Labib A, El-Saaidi C
    Dental Press J Orthod, 2019 1 24;23(6):40.e1-40.e10.
    PMID: 30672991 DOI: 10.1590/2177-6709.23.6.40.e1-10.onl
    OBJECTIVE: Considering that the available studies on prevalence of malocclusions are local or national-based, this study aimed to pool data to determine the distribution of malocclusion traits worldwide in mixed and permanent dentitions.

    METHODS: An electronic search was conducted using PubMed, Embase and Google Scholar search engines, to retrieve data on malocclusion prevalence for both mixed and permanent dentitions, up to December 2016.

    RESULTS: Out of 2,977 retrieved studies, 53 were included. In permanent dentition, the global distributions of Class I, Class II, and Class III malocclusion were 74.7% [31 - 97%], 19.56% [2 - 63%] and 5.93% [1 - 20%], respectively. In mixed dentition, the distributions of these malocclusions were 73% [40 - 96%], 23% [2 - 58%] and 4% [0.7 - 13%]. Regarding vertical malocclusions, the observed deep overbite and open bite were 21.98% and 4.93%, respectively. Posterior crossbite affected 9.39% of the sample. Africans showed the highest prevalence of Class I and open bite in permanent dentition (89% and 8%, respectively), and in mixed dentition (93% and 10%, respectively), while Caucasians showed the highest prevalence of Class II in permanent dentition (23%) and mixed dentition (26%). Class III malocclusion in mixed dentition was highly prevalent among Mongoloids.

    CONCLUSION: Worldwide, in mixed and permanent dentitions, Angle Class I malocclusion is more prevalent than Class II, specifically among Africans; the least prevalent was Class III, although higher among Mongoloids in mixed dentition. In vertical dimension, open bite was highest among Mongoloids in mixed dentition. Posterior crossbite was more prevalent in permanent dentition in Europe.

    Matched MeSH terms: Databases, Factual
  6. Ali BH, Sulaiman N, Al-Haddad SAR, Atan R, Hassan SLM, Alghrairi M
    Sensors (Basel), 2021 Sep 27;21(19).
    PMID: 34640773 DOI: 10.3390/s21196453
    One of the most dangerous kinds of attacks affecting computers is a distributed denial of services (DDoS) attack. The main goal of this attack is to bring the targeted machine down and make their services unavailable to legal users. This can be accomplished mainly by directing many machines to send a very large number of packets toward the specified machine to consume its resources and stop it from working. We implemented a method using Java based on entropy and sequential probabilities ratio test (ESPRT) methods to identify malicious flows and their switch interfaces that aid them in passing through. Entropy (E) is the first technique, and the sequential probabilities ratio test (SPRT) is the second technique. The entropy method alone compares its results with a certain threshold in order to make a decision. The accuracy and F-scores for entropy results thus changed when the threshold values changed. Using both entropy and SPRT removed the uncertainty associated with the entropy threshold. The false positive rate was also reduced when combining both techniques. Entropy-based detection methods divide incoming traffic into groups of traffic that have the same size. The size of these groups is determined by a parameter called window size. The Defense Advanced Research Projects Agency (DARPA) 1998, DARPA2000, and Canadian Institute for Cybersecurity (CIC-DDoS2019) databases were used to evaluate the implementation of this method. The metric of a confusion matrix was used to compare the ESPRT results with the results of other methods. The accuracy and f-scores for the DARPA 1998 dataset were 0.995 and 0.997, respectively, for the ESPRT method when the window size was set at 50 and 75 packets. The detection rate of ESPRT for the same dataset was 0.995 when the window size was set to 10 packets. The average accuracy for the DARPA 2000 dataset for ESPRT was 0.905, and the detection rate was 0.929. Finally, ESPRT was scalable to a multiple domain topology application.
    Matched MeSH terms: Databases, Factual
  7. Ali Mamat
    The technology of deductive database is now mature enough due to the considerable research efforts that have been made on the field for the last ten years. This achievement is demonstrated by the emergence of efficient and easy to use systems with their capability of supporting a declarative, rule based style of expressing queries and applications on databases. This paper describes an overview of architecture of a query evaluation system for deductive databases that has been developed.
    Teknologi pangkalan data deduktif sudah matang hasil daripada penyelidikan yang telah banyak dilakukan dalam tempoh 10 tahun yang lepas. Pencapaian ini dibuktikan melalui kemunculan sistem yang cekap dan mudah guna serta mempunyai keupayaan untuk mengungkap pertanyaan dan penggunaan ke atas pangkalan data secara deklaratif menerusi penggunaan petua. Dalam kertas ini diterangkan suatu ringkasan mengenai senibina sistem penilaian pertanyaan untuk pangkalan data deduktif yang sudah dibangunkan.
    Matched MeSH terms: Databases, Factual
  8. Ali Z, Elamvazuthi I, Alsulaiman M, Muhammad G
    J Voice, 2016 Nov;30(6):757.e7-757.e19.
    PMID: 26522263 DOI: 10.1016/j.jvoice.2015.08.010
    BACKGROUND AND OBJECTIVE: Automatic voice pathology detection using sustained vowels has been widely explored. Because of the stationary nature of the speech waveform, pathology detection with a sustained vowel is a comparatively easier task than that using a running speech. Some disorder detection systems with running speech have also been developed, although most of them are based on a voice activity detection (VAD), that is, itself a challenging task. Pathology detection with running speech needs more investigation, and systems with good accuracy (ACC) are required. Furthermore, pathology classification systems with running speech have not received any attention from the research community. In this article, automatic pathology detection and classification systems are developed using text-dependent running speech without adding a VAD module.

    METHOD: A set of three psychophysics conditions of hearing (critical band spectral estimation, equal loudness hearing curve, and the intensity loudness power law of hearing) is used to estimate the auditory spectrum. The auditory spectrum and all-pole models of the auditory spectrums are computed and analyzed and used in a Gaussian mixture model for an automatic decision.

    RESULTS: In the experiments using the Massachusetts Eye & Ear Infirmary database, an ACC of 99.56% is obtained for pathology detection, and an ACC of 93.33% is obtained for the pathology classification system. The results of the proposed systems outperform the existing running-speech-based systems.

    DISCUSSION: The developed system can effectively be used in voice pathology detection and classification systems, and the proposed features can visually differentiate between normal and pathological samples.

    Matched MeSH terms: Databases, Factual
  9. Ali Z, Alsulaiman M, Muhammad G, Elamvazuthi I, Al-Nasheri A, Mesallam TA, et al.
    J Voice, 2017 May;31(3):386.e1-386.e8.
    PMID: 27745756 DOI: 10.1016/j.jvoice.2016.09.009
    A large population around the world has voice complications. Various approaches for subjective and objective evaluations have been suggested in the literature. The subjective approach strongly depends on the experience and area of expertise of a clinician, and human error cannot be neglected. On the other hand, the objective or automatic approach is noninvasive. Automatic developed systems can provide complementary information that may be helpful for a clinician in the early screening of a voice disorder. At the same time, automatic systems can be deployed in remote areas where a general practitioner can use them and may refer the patient to a specialist to avoid complications that may be life threatening. Many automatic systems for disorder detection have been developed by applying different types of conventional speech features such as the linear prediction coefficients, linear prediction cepstral coefficients, and Mel-frequency cepstral coefficients (MFCCs). This study aims to ascertain whether conventional speech features detect voice pathology reliably, and whether they can be correlated with voice quality. To investigate this, an automatic detection system based on MFCC was developed, and three different voice disorder databases were used in this study. The experimental results suggest that the accuracy of the MFCC-based system varies from database to database. The detection rate for the intra-database ranges from 72% to 95%, and that for the inter-database is from 47% to 82%. The results conclude that conventional speech features are not correlated with voice, and hence are not reliable in pathology detection.
    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. Alofe O, Kisanga E, Inayat-Hussain SH, Fukumura M, Garcia-Milian R, Perera L, et al.
    Environ Int, 2019 10;131:104969.
    PMID: 31310931 DOI: 10.1016/j.envint.2019.104969
    Environmental and occupational exposure to industrial chemicals has been linked to toxic and carcinogenic effects in animal models and human studies. However, current toxicology testing does not thoroughly explore the endocrine disrupting effects of industrial chemicals, which may have low dose effects not predicted when determining the limit of toxicity. The objective of this study was to evaluate the endocrine disrupting potential of a broad range of chemicals used in the petrochemical sector. Therefore, 139 chemicals were classified for reproductive toxicity based on the United Nations Globally Harmonized System for hazard classification. These chemicals were evaluated in PubMed for reported endocrine disrupting activity, and their endocrine disrupting potential was estimated by identifying chemicals with active nuclear receptor endpoints publicly available databases. Evaluation of ToxCast data suggested that these chemicals preferentially alter the activity of the estrogen receptor (ER). Four chemicals were prioritized for in vitro testing using the ER-positive, immortalized human uterine Ishikawa cell line and a range of concentrations below the reported limit of toxicity in humans. We found that 2,6-di-tert-butyl-p-cresol (BHT) and diethanolamine (DEA) repressed the basal expression of estrogen-responsive genes PGR, NPPC, and GREB1 in Ishikawa cells, while tetrachloroethylene (PCE) and 2,2'-methyliminodiethanol (MDEA) induced the expression of these genes. Furthermore, low-dose combinations of PCE and MDEA produced additive effects. All four chemicals interfered with estradiol-mediated induction of PGR, NPPC, and GREB1. Molecular docking demonstrated that these chemicals could bind to the ligand binding site of ERα, suggesting the potential for direct stimulatory or inhibitory effects. We found that these chemicals altered rates of proliferation and regulated the expression of cell proliferation associated genes. These findings demonstrate previously unappreciated endocrine disrupting effects and underscore the importance of testing the endocrine disrupting potential of chemicals in the future to better understand their potential to impact public health.
    Matched MeSH terms: Databases, Factual*
  12. 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
  13. Altalib MK, Salim N
    Biomolecules, 2022 Nov 20;12(11).
    PMID: 36421733 DOI: 10.3390/biom12111719
    Information technology has become an integral aspect of the drug development process. The virtual screening process (VS) is a computational technique for screening chemical compounds in a reasonable amount of time and cost. The similarity search is one of the primary tasks in VS that estimates a molecule's similarity. It is predicated on the idea that molecules with similar structures may also have similar activities. Many techniques for comparing the biological similarity between a target compound and each compound in the database have been established. Although the approaches have a strong performance, particularly when dealing with molecules with homogenous active structural, they are not enough good when dealing with structurally heterogeneous compounds. The previous works examined many deep learning methods in the enhanced Siamese similarity model and demonstrated that the Enhanced Siamese Multi-Layer Perceptron similarity model (SMLP) and the Siamese Convolutional Neural Network-one dimension similarity model (SCNN1D) have good outcomes when dealing with structurally heterogeneous molecules. To further improve the retrieval effectiveness of the similarity model, we incorporate the best two models in one hybrid model. The reason is that each method gives good results in some classes, so combining them in one hybrid model may improve the retrieval recall. Many designs of the hybrid models will be tested in this study. Several experiments on real-world data sets were conducted, and the findings demonstrated that the new approaches outperformed the previous method.
    Matched MeSH terms: Databases, Factual
  14. Alyessary AS, Othman SA, Yap AUJ, Radzi Z, Rahman MT
    Int Orthod, 2019 03;17(1):12-19.
    PMID: 30732977 DOI: 10.1016/j.ortho.2019.01.001
    OBJECTIVE: This systematic review aims to determine the effects of non-surgical rapid maxillary expansion (RME) on breathing and upper airway structures.

    MATERIALS AND METHODS: An electronic search of the scientific literature from January 2005 to June 2016 was done using Web of Science, Dentistry & Oral Sciences Source and PubMed databases. A combination of search terms "rapid maxillary expansion", "nasal", "airway" and "breathing" were used. Studies that involved surgical or combined RME-surgical treatments and patients with craniofacial anomalies were excluded.

    RESULTS: The initial screening yielded a total of 183 articles. After evaluation of the titles, abstracts and accessing the full text, a total of 20 articles fulfilled both inclusion/exclusion criteria and possessed adequate evidence to be incorporated into this review.

    CONCLUSIONS: Non-surgical RME was found to improve breathing, increase nasal cavity geometry and decrease nasal airway resistance in children and adolescents.

    Matched MeSH terms: Databases, Factual
  15. Amelia TSM, Lau NS, Amirul AA, Bhubalan K
    Data Brief, 2020 Aug;31:105971.
    PMID: 32685631 DOI: 10.1016/j.dib.2020.105971
    Marine sponges are acknowledged as a bacterial hotspot and resource of novel natural products or genetic material with industrial or commercial potential. However, sponge-associated bacteria are difficult to be cultivated and the production of their desirable metabolites is inadequate in terms of rate and quantity, yet bioinformatics and metagenomics tools are steadily progressing. Bacterial diversity profiles of high-microbial-abundance wild tropical marine sponges Aaptos aaptos and Xestospongia muta were obtained by sample collection at Pulau Bidong and Pulau Redang islands, 16S rRNA amplicon sequencing on Illumina HiSeq2500 platform (250 bp paired-end) and metagenomics analysis using Ribosomal Database Project (RDP) classifier. Raw sequencing data in fastq format and relative abundance histograms of the dominant 10 species are available in the public repository Discover Mendeley Data (http://dx.doi.org/10.17632/zrcks5s8xp). Filtered sequencing data of operational taxonomic unit (OTU) with chimera removed is available in NCBI accession numbers from MT464469 to MT465036.
    Matched MeSH terms: Databases, Factual
  16. Anuar A, Marwan NF, Smith J, Siriyanun S, Sharif A
    Environ Sci Pollut Res Int, 2022 Feb;29(9):13729-13741.
    PMID: 34599441 DOI: 10.1007/s11356-021-16470-1
    The aim of this paper is to examine immigration and environmental degradation using bibliometric analysis. This paper also analyzes sources of publication, authorship, citations, distributions publications and other bibliometric indicators. The study focuses on a total of 1372 articles published from 2000 to 2020. These articles were collected through an automated process from the Scopus database and later analyzed using techniques such as bibliometric indicators analysis, VOSviewer, and Perish or Publish. The research identified 991 articles from varieties of published sources. The topic of immigrants and environmental degradation has been an emerging topic since 1981. Starting in 2000, most of the scholars actively producing an articles pertinent to this topic. Most of the articles were published in journals, and English is the primary language of research. United States is the leading country in contributing the publications. Meanwhile, the most significant fields in which the sources were produced were environmental science, agricultural and biological sciences, arts and humanities and earth and planetary sciences. However, some limitations has been found. It has been suggested for future research, to lengthen this work to other databases, as well as bibliometric analyses of immigration and environmental degradation in developed and developing countries by adding a new keyword such as energy consumption and climate change. This paper aims to assess recent trends in the expansion of academic literature on immigration and environmental degradation using the bibliometric analysis method. Network visualization and bibliometric indicators are used in this paper to present the results.
    Matched MeSH terms: Databases, Factual
  17. Aqra I, Herawan T, Abdul Ghani N, Akhunzada A, Ali A, Bin Razali R, et al.
    PLoS One, 2018;13(1):e0179703.
    PMID: 29351287 DOI: 10.1371/journal.pone.0179703
    Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.
    Matched MeSH terms: Databases, Factual
  18. Arif SM, Holliday JD, Willett P
    J Chem Inf Model, 2010 Aug 23;50(8):1340-9.
    PMID: 20672867 DOI: 10.1021/ci1001235
    This paper discusses the weighting of two-dimensional fingerprints for similarity-based virtual screening, specifically the use of weights that assign greatest importance to the substructural fragments that occur least frequently in the database that is being screened. Virtual screening experiments using the MDL Drug Data Report and World of Molecular Bioactivity databases show that the use of such inverse frequency weighting schemes can result, in some circumstances, in marked increases in screening effectiveness when compared with the use of conventional, unweighted fingerprints. Analysis of the characteristics of the various schemes demonstrates that such weights are best used to weight the fingerprint of the reference structure in a similarity search, with the database structures' fingerprints unweighted. However, the increases in performance resulting from such weights are only observed with structurally homogeneous sets of active molecules; when the actives are diverse, the best results are obtained using conventional, unweighted fingerprints for both the reference structure and the database structures.
    Matched MeSH terms: Databases, Factual
  19. Arnia F, Oktiana M, Saddami K, Munadi K, Roslidar R, Pradhan B
    Sensors (Basel), 2021 Jul 04;21(13).
    PMID: 34283116 DOI: 10.3390/s21134575
    Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition. In this paper, we proposed a phase-based CSCD face recognition approach. We employed Homomorphic filtering as photometric normalization and Band Limited Phase Only Correlation (BLPOC) for image matching. Different from the state-of-the-art methods, we directly utilized the phase component from an image, without the need for a feature extraction process. The experiment was conducted using the Long-Distance Heterogeneous Face Database (LDHF-DB). The proposed method was evaluated in three scenarios: (i) cross-spectral face verification at 1m, (ii) cross-spectral face verification at 60m, and (iii) cross-spectral face verification where the probe images (near-infrared (NIR) face images) were captured at 1m and the gallery data (face images) was captured at 60 m. The proposed CSCD method resulted in the best recognition performance among the CSCD baseline approaches, with an Equal Error Rate (EER) of 5.34% and a Genuine Acceptance Rate (GAR) of 93%.
    Matched MeSH terms: Databases, Factual
  20. Arunah C, Feisul IM, Nor Saleha IT, Muhammad Radzi AH
    Med J Malaysia, 2020 05;75(3):235-239.
    PMID: 32467538
    INTRODUCTION: Colorectal cancer (CRC) is the second most common cancer in Malaysia with 65% detected at stage III and IV. Despite the increasing incidence of cancers including CRC, Malaysia has yet to implement populationbased screening for cancers. The objective of this paper is to review the strategic planning and implementation of the CRC screening program in Malaysia.

    METHODS: A desk review was conducted from August to October in 2018, to examine, review and describe the historical perspective, strategic planning and implementation of the current CRC screening program in Malaysia.

    RESULTS: The main policy documents related to CRC screening are the National Strategic Plan for Cancer Control Programme 2016-2020, the Clinical Practice Guideline for Management of Colorectal Carcinoma 2017, and the Implementation Guideline for CRC Screening in Malaysia 2014. Several papers have been published on the epidemiology of CRC in Malaysia. Between 2014 and 2018, 127,957 men and women were screened using immunochemical Faecal Occult Blood Test (iFOBT); 9.3% had positive iFOBT results and were referred for colonoscopy. For those who underwent colonoscopy, CRC detection rate was 4.1% and 13.9% for pre-malignant conditions. Barriers were identified along the continuum of screening process, including patient, provider, and system factors.

    CONCLUSION: Although population-level organised screening programmes are preferable to opportunistic screening, the CRC programme in Malaysia was tailored to meet the needs of the population based on available existing resources. A well-mapped budget for the entire screening programme continuum, a strong partnership between stakeholders and an opportunistic screening strategy is crucial to address the rising incidence of CRC.

    Matched MeSH terms: Databases, Factual
Filters
Contact Us

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

External Links