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Fulltext Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm

Abdulrauf Sharifai G, Zainol Z

Genes (Basel), 2020 06 27;11(7).
PMID: 32605144 DOI: 10.3390/genes11070717

The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced data set has posed severe challenges in many real-world applications, such as biomedical data sets. Numerous researchers investigated either imbalanced class or high dimensional data sets and came up with various methods. Nonetheless, few approaches reported in the literature have addressed the intersection of the high dimensional and imbalanced class problem due to their complicated interactions. Lately, feature selection has become a well-known technique that has been used to overcome this problem by selecting discriminative features that represent minority and majority class. This paper proposes a new method called Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm (rCBR-BGOA); rCBR-BGOA has employed an ensemble of multi-filters coupled with the Correlation-Based Redundancy method to select optimal feature subsets. A binary Grasshopper optimisation algorithm (BGOA) is used to construct the feature selection process as an optimisation problem to select the best (near-optimal) combination of features from the majority and minority class. The obtained results, supported by the proper statistical analysis, indicate that rCBR-BGOA can improve the classification performance for high dimensional and imbalanced datasets in terms of G-mean and the Area Under the Curve (AUC) performance metrics.

Matched MeSH terms: Data Accuracy*
Fulltext The importance of human capital perspective in the learning management system (LMS) decision making process at universities

Norhaiza K, Rozainee K, Mohd Noor Abdul H, Phillip H, McGill T, Zainah Ahmad Z

Jurnal Psikologi Malaysia, 2016;30:102-113.

This research examined how managers in universities incorporate non-financial measures in their Learning Management Systems decision-making processes and particularly focused on the importance of the Human Capital perspective in LMS decision making processes. A mixed-methods approach to data collection was used involving both interviews and questionnaires. The qualitative data from the interviews were coded and analysed. A descriptive coding method using thematic analysis was used for the data coding. The qualitative data were analysed using an inductive approach where the categories of criteria and indicators were not determined before the interview. The participants in this research were five members of LMS decision-making teams at two different universities in Australia and 24 participants from different universities in Malaysia who were involved in LMS decision- making processes at their universities. The results of this research indicated that there was substantial support for using a multi-dimensional decision making model among IT decision makers at universities, particularly the Human Capital perspective and they believed that Human Capital measures are important and should be considered in a LMS decision making process.The research has both implications for theory and for practitioners where it contributes to the knowledge on LMS decision making in universities and IT decision making in general, and also in improving actual decision making practices.

Matched MeSH terms: Data Accuracy
Fulltext Development of smart wireless detector system for gamma ray detection

Nolida Yussup, Nur Aira Abd. Rahman, Ismail Mustapha, Jaafar Abdullah, Mohd. Ashhar Khalid, Hearie Hassan, et al.

Jurnal Sains Nuklear Malaysia, 2012;24(1):43-47.
MyJurnal

Data transmission in field works especially that is related to industry, gas and chemical is paramount importance to ensure data accuracy and delivery time. A development of wireless detector system for remote data acquisition to be applied in conducting fieldwork in industry is described in this paper. A wireless communication which is applied in the project development is a viable and cost-effective method of transmitting data from the detector to the laptop on the site to facilitate data storage and analysis automatically, which can be used in various applications such as column scanning. The project involves hardware design for the detector and electronics parts besides programming for control board and user interface. A prototype of a wireless gamma scintillation detector is developed with capabilities of transmitting data to computer via radio frequency (RF) and recording the data within the 433MHz band at baud rate of 19200.

Matched MeSH terms: Data Accuracy
Errors in accident data, its types, causes and methods of rectification-analysis of the literature

Ahmed A, Sadullah AFM, Yahya AS

Accid Anal Prev, 2019 Sep;130:3-21.
PMID: 28764851 DOI: 10.1016/j.aap.2017.07.018

Most of the decisions taken to improve road safety are based on accident data, which makes it the back bone of any country's road safety system. Errors in this data will lead to misidentification of black spots and hazardous road segments, projection of false estimates pertinent to accidents and fatality rates, and detection of wrong parameters responsible for accident occurrence, thereby making the entire road safety exercise ineffective. Its extent varies from country to country depending upon various factors. Knowing the type of error in the accident data and the factors causing it enables the application of the correct method for its rectification. Therefore there is a need for a systematic literature review that addresses the topic at a global level. This paper fulfils the above research gap by providing a synthesis of literature for the different types of errors found in the accident data of 46 countries across the six regions of the world. The errors are classified and discussed with respect to each type and analysed with respect to income level; assessment with regard to the magnitude for each type is provided; followed by the different causes that result in their occurrence, and the various methods used to address each type of error. Among high-income countries the extent of error in reporting slight, severe, non-fatal and fatal injury accidents varied between 39-82%, 16-52%, 12-84%, and 0-31% respectively. For middle-income countries the error for the same categories varied between 93-98%, 32.5-96%, 34-99% and 0.5-89.5% respectively. The only four studies available for low-income countries showed that the error in reporting non-fatal and fatal accidents varied between 69-80% and 0-61% respectively. The logistic relation of error in accident data reporting, dichotomised at 50%, indicated that as the income level of a country increases the probability of having less error in accident data also increases. Average error in recording information related to the variables in the categories of location, victim's information, vehicle's information, and environment was 27%, 37%, 16% and 19% respectively. Among the causes identified for errors in accident data reporting, Policing System was found to be the most important. Overall 26 causes of errors in accident data were discussed out of which 12 were related to reporting and 14 were related to recording. "Capture-Recapture" was the most widely used method among the 11 different methods: that can be used for the rectification of under-reporting. There were 12 studies pertinent to the rectification of accident location and almost all of them utilised a Geographical Information System (GIS) platform coupled with a matching algorithm to estimate the correct location. It is recommended that the policing system should be reformed and public awareness should be created to help reduce errors in accident data.

Matched MeSH terms: Data Accuracy*
Real-world data for health technology assessment for reimbursement decisions in Asia: current landscape and a way forward

Lou J, Kc S, Toh KY, Dabak S, Adler A, Ahn J, et al.

Int J Technol Assess Health Care, 2020 Oct;36(5):474-480.
PMID: 32928330 DOI: 10.1017/S0266462320000628

There is growing interest globally in using real-world data (RWD) and real-world evidence (RWE) for health technology assessment (HTA). Optimal collection, analysis, and use of RWD/RWE to inform HTA requires a conceptual framework to standardize processes and ensure consistency. However, such framework is currently lacking in Asia, a region that is likely to benefit from RWD/RWE for at least two reasons. First, there is often limited Asian representation in clinical trials unless specifically conducted in Asian populations, and RWD may help to fill the evidence gap. Second, in a few Asian health systems, reimbursement decisions are not made at market entry; thus, allowing RWD/RWE to be collected to give more certainty about the effectiveness of technologies in the local setting and inform their appropriate use. Furthermore, an alignment of RWD/RWE policies across Asia would equip decision makers with context-relevant evidence, and improve timely patient access to new technologies. Using data collected from eleven health systems in Asia, this paper provides a review of the current landscape of RWD/RWE in Asia to inform HTA and explores a way forward to align policies within the region. This paper concludes with a proposal to establish an international collaboration among academics and HTA agencies in the region: the REAL World Data In ASia for HEalth Technology Assessment in Reimbursement (REALISE) working group, which seeks to develop a non-binding guidance document on the use of RWD/RWE to inform HTA for decision making in Asia.

Matched MeSH terms: Data Accuracy
Evaluation of Development Datasets for Hermetia illucens (L.) (Diptera: Stratiomyidae) for Estimating the Time of Placement of Human and Swine Remains in Texas, USA

Cuttiford L, Pimsler ML, Heo CC, Zheng L, Karunaratne I, Trissini G, et al.

J Med Entomol, 2021 07 16;58(4):1654-1662.
PMID: 33970239 DOI: 10.1093/jme/tjab081

A basic tenet of forensic entomology is development data of an insect can be used to predict the time of colonization (TOC) by insect specimens collected from remains, and this prediction is related to the time of death and/or time of placement (TOP). However, few datasets have been evaluated to determine their accuracy or precision. The black soldier fly, Hermetia illucens (L.) (Diptera: Stratiomyidae) is recognized as an insect of forensic importance. This study examined the accuracy and precision of several development datasets for the black soldier fly by estimating the TOP of five sets of human and three sets of swine remains in San Marcos and College Station, TX, respectively. Data generated from this study indicate only one of these datasets consistently (time-to-prepupae 52%; time-to-eclosion 75%) produced TOP estimations that occurred within a day of the actual TOP of the remains. It is unknown if the precolonization interval (PreCI) of this species is long, but it has been observed that the species can colonize within 6 d after death. This assumption remains untested by validation studies. Accounting for this PreCI improved accuracy for the time-to-prepupae group, but reduced accuracy in the time-to-eclosion group. The findings presented here highlight a need for detailed, forensic-based development data for the black soldier fly that can reliably and accurately be used in casework. Finally, this study outlines the need for a basic understanding of the timing of resource utilization (i.e., duration of the PreCI) for forensically relevant taxa so that reasonable corrections may be made to TOC as related to minimum postmortem interval (mPMI) estimates.

Matched MeSH terms: Data Accuracy
Fulltext Orphan drugs and high cost medications: challenges and solutions

Chuah, S.Y., Thong, M.K.

JUMMEC, 2018;21(2):53-58.
MyJurnal

There had been increased and strong public interests in rare diseases and orphan drugs as well as the issue of
compulsory licencing for expensive medications in Malaysia in the mass-media and social media. We reviewed
the issues of orphan drugs and the challenges faced in many countries in developing appropriate health financial
modelling as well as getting accurate data on rare diseases. We also reviewed the old off-patent medications
and the developments on how policy-makers can intervene to make expensive treatment affordable and
sustainable for patients and the country.

Matched MeSH terms: Data Accuracy
Fulltext "It's Valid and Reliable" Is Not Enough: Critical Appraisal of Reporting of Measures in Trials Evaluating Patient Decision Aids

Sepucha KR, Matlock DD, Wills CE, Ropka M, Joseph-Williams N, Stacey D, et al.

Med Decis Making, 2014 07;34(5):560-6.
PMID: 24713692 DOI: 10.1177/0272989X14528381

BACKGROUND: This review systematically appraises the quality of reporting of measures used in trials to evaluate the effectiveness of patient decision aids (PtDAs) and presents recommendations for minimum reporting standards.
METHODS: We reviewed measures of decision quality and decision process in 86 randomized controlled trials (RCTs) from the 2011 Cochrane Collaboration systematic review of PtDAs. Data on development of the measures, reliability, validity, responsiveness, precision, interpretability, feasibility, and acceptability were independently abstracted by 2 reviewers.
RESULTS: Information from 178 instances of use of measures was abstracted. Very few studies reported data on the performance of measures, with reliability (21%) and validity (16%) being the most common. Studies using new measures were less likely to include information about their psychometric performance. The review was limited to reporting of measures in studies included in the Cochrane review and did not consult prior publications.
CONCLUSIONS: Very little is reported about the development or performance of measures used to evaluate the effectiveness of PtDAs in published trials. Minimum reporting standards are proposed to enable authors to prepare study reports, editors and reviewers to evaluate submitted papers, and readers to appraise published studies.

Matched MeSH terms: Data Accuracy
Assessing primary care data quality

Lim YMF, Yusof M, Sivasampu S

Int J Health Care Qual Assur, 2018 Apr 16;31(3):203-213.
PMID: 29687760 DOI: 10.1108/IJHCQA-08-2016-0111

Purpose The purpose of this paper is to assess National Medical Care Survey data quality. Design/methodology/approach Data completeness and representativeness were computed for all observations while other data quality measures were assessed using a 10 per cent sample from the National Medical Care Survey database; i.e., 12,569 primary care records from 189 public and private practices were included in the analysis. Findings Data field completion ranged from 69 to 100 per cent. Error rates for data transfer from paper to web-based application varied between 0.5 and 6.1 per cent. Error rates arising from diagnosis and clinical process coding were higher than medication coding. Data fields that involved free text entry were more prone to errors than those involving selection from menus. The authors found that completeness, accuracy, coding reliability and representativeness were generally good, while data timeliness needs to be improved. Research limitations/implications Only data entered into a web-based application were examined. Data omissions and errors in the original questionnaires were not covered. Practical implications Results from this study provided informative and practicable approaches to improve primary health care data completeness and accuracy especially in developing nations where resources are limited. Originality/value Primary care data quality studies in developing nations are limited. Understanding errors and missing data enables researchers and health service administrators to prevent quality-related problems in primary care data.

Matched MeSH terms: Data Accuracy*
Fulltext Modeling the Physical Properties of Gamma Alumina Catalyst Carrier Based on an Artificial Neural Network

Majdi HS, Saud AN, Saud SN

Materials (Basel), 2019 May 29;12(11).
PMID: 31146451 DOI: 10.3390/ma12111752

Porous γ-alumina is widely used as a catalyst carrier due to its chemical properties. These properties are strongly correlated with the physical properties of the material, such as porosity, density, shrinkage, and surface area. This study presents a technique that is less time consuming than other techniques to predict the values of the above-mentioned physical properties of porous γ-alumina via an artificial neural network (ANN) numerical model. The experimental data that was implemented was determined based on 30 samples that varied in terms of sintering temperature, yeast concentration, and socking time. Of the 30 experimental samples, 25 samples were used for training purposes, while the other five samples were used for the execution of the experimental procedure. The results showed that the prediction and experimental data were in good agreement, and it was concluded that the proposed model is proficient at providing high accuracy estimation data derived from any complex analytical equation.

Matched MeSH terms: Data Accuracy
Fulltext Out of hospital cardiac arrest in Kuala Lumpur: incidence; adherence to protocol; and issues: a mixed method study

Mohd Said Nurumal, Sarah Sheikh Abdul Karim

Malaysian Journal of Public Health Medicine, 2015;15(3):0-0.
MyJurnal

Information regarding out of hospital cardiac arrest incidence including outcomes in Malaysia is limited and fragmented. This study aims to identify the incidence and adherence to protocol of out of hospital cardiac arrest and also to explore the issues faced by pre-hospital personnel in regards to the management of cardiac arrest victim in Kuala Lumpur, Malaysia. A mixed method approach combining qualitative and quantitative study design was used. Two hundred eighty five (285) pre-hospital care data sheet for out of hospital cardiac arrest during the year of 2011 were examined by using checklists to identify the incidence and adherence to protocol. Nine semi-structured interviews and two focus group discussions were performed. Based on the overall incidence for out of hospital cardiac arrest cases which occurred in 2011 (n=285), the survival rate was 16.8%. On the adherence to protocol, only 89 (41.8%) of the cases adhered to the given protocol and 124 did not adhere to such protocol. All the relevant qualitative data were merged into few categories relating to issues that could affect the management of out of hospital cardiac arrest performed by pre-hospital care team. The essential elements in the handling of out of hospital cardiac arrest by pre-hospital care teamwasto ensure increased survival rates and excellent outcomes. Measures are needed to strengthen the quick activation of the pre-hospital care service, prompt bystander cardiopulmonary resuscitation, early defibrillation and timely advanced cardiac life support, and also to address all other issues highlighted in the qualitative results of this study.

Matched MeSH terms: Data Accuracy
Fulltext Vision-Based Apple Classification for Smart Manufacturing

Ismail A, Idris MYI, Ayub MN, Por LY

Sensors (Basel), 2018 Dec 10;18(12).
PMID: 30544660 DOI: 10.3390/s18124353

Smart manufacturing enables an efficient manufacturing process by optimizing production and product transaction. The optimization is performed through data analytics that requires reliable and informative data as input. Therefore, in this paper, an accurate data capture approach based on a vision sensor is proposed. Three image recognition methods are studied to determine the best vision-based classification technique, namely Bag of Words (BOW), Spatial Pyramid Matching (SPM) and Convolutional Neural Network (CNN). The vision-based classifiers categorize the apple as defective and non-defective that can be used for automatic inspection, sorting and further analytics. A total of 550 apple images are collected to test the classifiers. The images consist of 275 non-defective and 275 defective apples. The defective category includes various types of defect and severity. The vision-based classifiers are trained and evaluated according to the K-fold cross-validation. The performances of the classifiers from 2-fold, 3-fold, 4-fold, 5-fold and 10-fold are compared. From the evaluation, SPM with SVM classifier attained 98.15% classification accuracy for 10-fold and outperformed the others. In terms of computational time, CNN with SVM classifier is the fastest. However, minimal time difference is observed between the computational time of CNN and SPM, which were separated by only 0.05 s.

Matched MeSH terms: Data Accuracy
Fulltext Life After Death in a Chinese Community

MOHD HAFIZOL AMIN BIN RAMLI, NUR AMELIA BINTI MAZLAN, SITI AIDA BINTI AZMI, MUHD SARJI BIN AWANG BULAT, PARVEEN KAUR

Trends in Undergraduate Research, 2018;1(1):0-0.
MyJurnal

According to San Fillipo (2006), death is not the end of one’s existence, but rather than a transition from one life to another. However, it is different based on how the society and individuals see the concept of death itself and how they understand about it. Thus, this article aims to explore the understanding of the relationship between culture and religion that become their identity in terms of death and life after. Qualitative approach is adopted for this study. Indeed, interview and empirical observation were used to obtain quality data.

Matched MeSH terms: Data Accuracy
Label-free optical spectroscopy for characterizing binding properties of highly sensitive nanocrystalline cellulose-graphene oxide based nanocomposite towards nickel ion

Daniyal WMEMM, Fen YW, Abdullah J, Sadrolhosseini AR, Saleviter S, Omar NAS

Spectrochim Acta A Mol Biomol Spectrosc, 2019 Apr 05;212:25-31.
PMID: 30594850 DOI: 10.1016/j.saa.2018.12.031

Surface plasmon resonance (SPR) is a label-free optical spectroscopy that is widely used for biomolecular interaction analysis. In this work, SPR was used to characterize the binding properties of highly sensitive nanocrystalline cellulose-graphene oxide based nanocomposite (CTA-NCC/GO) towards nickel ion. The formation of CTA-NCC/GO nanocomposite has been confirmed by FT-IR. The SPR analysis result shows that the CTA-NCC/GO has high binding affinity towards Ni2+ from 0.01 until 0.1 ppm with binding affinity constant of 1.620 × 103 M-1. The sensitivity for the CTA-NCC/GO calculated was 1.509° ppm-1. The full width at half maximum (FWHM), data accuracy (DA), and signal-to-noise ratio (SNR) have also been determined using the obtained SPR curve. For the FWHM, the value was 2.25° at 0.01 until 0.08 ppm and decreases to 2.12° at 0.1 until 10 ppm. The DA for the SPR curves is the highest at 0.01 until 0.08 ppm and lowest at 0.1 until 10 ppm. The SNR curves mirrors the curves of SPR angle shift where the SNR increases with the Ni2+ concentrations. For the selectivity test, the CTA-NCC/GO has the abilities to differentiate Ni2+ in the mixture of metal ions.

Matched MeSH terms: Data Accuracy
Fulltext Performance of women entrepreneurs: a result on formation of human capital and emulous factors

Nor Azira Ayob, Sity Daud, Nurul Nadia Abu Hassan

Malaysian Journal of Social Sciences and Humanities, 2018;3(3):173-188.
MyJurnal

Human resource development comprises skills, abilities, creativity and talent are amongst factors in
human capital as well as emulous aspect. Hence, the emphasis on human capital development and
emulous aspect is important to become a good leader for family, community and country. It is also
important in ensuring entrepreneurs can compete in market economy today and they are able to meet
customer demand. Thus, the objectives are the emphasis on the factors that are able to contribute in
improving human capital and emulous of women. This is because, the right factor will enable the government to carry out in accordance with the factors that have been identified. In obtaining the
factors contribute to human capital development, the survey method was conducted on 145 respondents
among Bumiputera women entrepreneurs in Melaka state and supported with qualitative data from 10
informants. The findings through exploratory factor analysis found that there are four main factors that
contribute to human capital development among Bumiputera women entrepreneurs which are
education and training, experience, social support and creativity, while three main factors that
contribute to emulous among Bumiputera women entrepreneurs are financial assistance, facilities and
infrastructure and commitment. Thus, the government is advised to emphasize on education and
training as well as financial assistance to improve their abilities on human capital and emulous that is
appropriate to support the women entrepreneurs need to increase their performance.

Matched MeSH terms: Data Accuracy
A Comparison of Various Imputation Methods for Missing Values in Air Quality Data

Nuryazmin Ahmat Zainuri, Abdul Aziz Jemain, Nora Muda

Sains Malaysiana, 2015;44:449-456.

This paper presents various imputation methods for air quality data specifically in Malaysia. The main objective was to
select the best method of imputation and to compare whether there was any difference in the methods used between stations
in Peninsular Malaysia. Missing data for various cases are randomly simulated with 5, 10, 15, 20, 25 and 30% missing.
Six methods used in this paper were mean and median substitution, expectation-maximization (EM) method, singular
value decomposition (SVD), K-nearest neighbour (KNN) method and sequential K-nearest neighbour (SKNN) method. The
performance of the imputations is compared using the performance indicator: The correlation coefficient (R), the index
of agreement (d) and the mean absolute error (MAE). Based on the result obtained, it can be concluded that EM, KNN
and SKNN are the three best methods. The same result are obtained for all the eight monitoring station used in this study.

Matched MeSH terms: Data Accuracy
Fulltext Medical students' knowledge and perception toward family planning services: A preliminary intervention study

Htay MNN, Latt SS, Abas AL, Chuni N, Soe HHK, Moe S

J Educ Health Promot, 2018;7:137.
PMID: 30596109 DOI: 10.4103/jehp.jehp_104_18

INTRODUCTION: Family planning and contraception is the effective strategy to reduce maternal mortality, child mortality, abortion, and unwanted pregnancies. Since the medical students are the future doctors, it is important to have proper knowledge and training on family planning services. This study aimed to explore the effect of teaching-learning process at maternal and child health (MCH) clinics on the students' knowledge, perceptions toward contraception methods, and family planning counselling.
METHODS: This quasi-experimental study was conducted in the private medical institution in Malaysia. The same questionnaire was used to administer twice, before and after the posting. Moreover, a qualitative question on the issues related to family planning and contraception utilizations in Malaysia was added to the after posting survey. The quantitative data were analyzed using IBM SPSS (version 20) and qualitative data by RQDA software.
RESULTS: A total of 146 participants were recruited in this study. Knowledge on contraception method before posting was 5.11 (standard deviation [SD] ±1.36) and after posting was 6.35 (SD ± 1.38) (P < 0.001). Thematic analysis of the students' answer revealed four salient themes, which were as follows: (1) cultural barrier, (2) misconception, (3) inadequate knowledge, and (4) improvement for the health-care services.
CONCLUSIONS: The teaching-learning process at the MCH posting has an influence on their perception and upgraded their knowledge. It also reflects the role of primary health-care clinics on medical students' clinical exposure and training on family planning services during their postings.

Matched MeSH terms: Data Accuracy
Improving routine health data in Indonesia: Utilising the WHO data quality tool for Aplikasi Satu Data Kesehatan

Sanjaya GY, Fauziah K, Pratama RA, Fitriani NA, Setiawan MY, Fauziah IA, et al.

Med J Malaysia, 2024 Mar;79(2):176-183.
PMID: 38553923

INTRODUCTION: Assessment of data quality in the era of big data is crucial for effective data management and use. However, there are gaps in data quality assessment for routine health data to ensure accountability. Therefore, this research aims to improve the routine health data quality that have been collected and integrated into Aplikasi Satu Data Kesehatan (ASDK) as the primary health data system in Indonesia.
MATERIALS AND METHODS: This descriptive study utilises a desk review approach and employs the WHO Data Quality Assurance (DQA) Tool to assess data quality of ASDK. The analysis involves measuring eight health indicators from ASDK and Survei Status Gizi Indonesia (SSGI) conducted in 2022. The assessment focuses on various dimensions of data quality, including completeness of variables, consistency over time, consistency between indicators, outliers and external consistency.
RESULTS: Current study shows that routine health data in Indonesia performs high-quality data in terms of completeness and internal consistency. The dimension of data completeness demonstrates high levels of variable completeness with most variables achieving 100% of the completeness.
CONCLUSION: Based on the analysis of eight routine health data variables using five dimensions of data quality namely completeness of variables, consistency over time, consistency between indicators, outliers. and external consistency. It shows that completeness and internal consistency of data in ASDK has demonstrated a high data quality.

Matched MeSH terms: Data Accuracy*
Fulltext Pattern Matching for DNA Sequencing Data Using Multiple Bloom Filters

Najam M, Rasool RU, Ahmad HF, Ashraf U, Malik AW

Biomed Res Int, 2019;2019:7074387.
PMID: 31111064 DOI: 10.1155/2019/7074387

Storing and processing of large DNA sequences has always been a major problem due to increasing volume of DNA sequence data. However, a number of solutions have been proposed but they require significant computation and memory. Therefore, an efficient storage and pattern matching solution is required for DNA sequencing data. Bloom filters (BFs) represent an efficient data structure, which is mostly used in the domain of bioinformatics for classification of DNA sequences. In this paper, we explore more dimensions where BFs can be used other than classification. A proposed solution is based on Multiple Bloom Filters (MBFs) that finds all the locations and number of repetitions of the specified pattern inside a DNA sequence. Both of these factors are extremely important in determining the type and intensity of any disease. This paper serves as a first effort towards optimizing the search for location and frequency of substrings in DNA sequences using MBFs. We expect that further optimizations in the proposed solution can bring remarkable results as this paper presents a proof of concept implementation for a given set of data using proposed MBFs technique. Performance evaluation shows improved accuracy and time efficiency of the proposed approach.

Matched MeSH terms: Data Accuracy
Crash severity along rural mountainous highways in Malaysia: An application of a combined decision tree and logistic regression model

Rusli R, Haque MM, Saifuzzaman M, King M

Traffic Inj Prev, 2018;19(7):741-748.
PMID: 29932734 DOI: 10.1080/15389588.2018.1482537

OBJECTIVE: Traffic crashes along mountainous highways may lead to injuries and fatalities more often than along highways on plain topography; however, research focusing on the injury outcome of such crashes is relatively scant. The objective of this study was to investigate the factors affecting the likelihood that traffic crashes along rural mountainous highways result in injuries.
METHOD: This study proposes a combination of decision tree and logistic regression techniques to model crash severity (injury vs. noninjury), because the combined approach allows the specification of nonlinearities and interactions in addition to main effects. Both a scobit model and a random parameters logit model, respectively accounting for an imbalance response variable and unobserved heterogeneities, are tested and compared. The study data set contains a total of 5 years of crash data (2008-2012) on selected mountainous highways in Malaysia. To enrich the data quality, an extensive field survey was conducted to collect detailed information on horizontal alignment, longitudinal grades, cross-section elements, and roadside features. In addition, weather condition data from the meteorology department were merged using the time stamp and proximity measures in AutoCAD-Geolocation.
RESULTS: The random parameters logit model is found to outperform both the standard logit and scobit models, suggesting the importance of accounting for unobserved heterogeneity in crash severity models. Results suggest that proportion of segment lengths with simple curves, presence of horizontal curves along steep gradients, highway segments with unsealed shoulders, and highway segments with cliffs along both sides are positively associated with injury-producing crashes along rural mountainous highways. Interestingly, crashes during rainy conditions are associated with crashes that are less likely to involve injury. It is also found that the likelihood of injury-producing crashes decreases for rear-end collisions but increases for head-on collisions and crashes involving heavy vehicles. A higher order interaction suggests that single-vehicle crashes involving light and medium-sized vehicles are less severe along straight sections compared to road sections with horizontal curves. One the other hand, crash severity is higher when heavy vehicles are involved in crashes as single vehicles traveling along straight segments of rural mountainous highways.
CONCLUSION: In addition to unobserved heterogeneity, it is important to account for higher order interactions to have a better understanding of factors that influence crash severity. A proper understanding of these factors will help develop targeted countermeasures to improve road safety along rural mountainous highways.

Matched MeSH terms: Data Accuracy

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