Displaying publications 1 - 20 of 77 in total

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  1. Applying data mining in healthcare: an info-structure for delivering 'data-driven' strategic services
    Abidi SS
    Stud Health Technol Inform, 1999;68:453-6.
    PMID: 10724926
    Presently, there is a growing demand from the healthcare community to leverage upon and transform the vast quantities of healthcare data into value-added, 'decision-quality' knowledge, vis-à-vis, strategic knowledge services oriented towards healthcare management and planning. To meet this end, we present a Strategic Knowledge Services Info-structure that leverages on existing healthcare knowledge/data bases to derive decision-quality knowledge-knowledge that is extracted from healthcare data through services akin to knowledge discovery in databases and data mining.
    Matched MeSH terms: Information Storage and Retrieval/statistics & numerical data*
  2. Landslide spatial modelling using novel bivariate statistical based Naïve Bayes, RBF Classifier, and RBF Network machine learning algorithms
    He Q, Shahabi H, Shirzadi A, Li S, Chen W, Wang N, et al.
    Sci Total Environ, 2019 May 01;663:1-15.
    PMID: 30708212 DOI: 10.1016/j.scitotenv.2019.01.329
    Landslides are major hazards for human activities often causing great damage to human lives and infrastructure. Therefore, the main aim of the present study is to evaluate and compare three machine learning algorithms (MLAs) including Naïve Bayes (NB), radial basis function (RBF) Classifier, and RBF Network for landslide susceptibility mapping (LSM) at Longhai area in China. A total of 14 landslide conditioning factors were obtained from various data sources, then the frequency ratio (FR) and support vector machine (SVM) methods were used for the correlation and selection the most important factors for modelling process, respectively. Subsequently, the resulting three models were validated and compared using some statistical metrics including area under the receiver operating characteristics (AUROC) curve, and Friedman and Wilcoxon signed-rank tests The results indicated that the RBF Classifier model had the highest goodness-of-fit and performance based on the training and validation datasets. The results concluded that the RBF Classifier model outperformed and outclassed (AUROC = 0.881), the NB (AUROC = 0.872) and the RBF Network (AUROC = 0.854) models. The obtained results pointed out that the RBF Classifier model is a promising method for spatial prediction of landslide over the world.
    Matched MeSH terms: Information Storage and Retrieval
  3. Fulltext Effect of temporal relationships in associative rule mining for web log data
    Khairudin NM, Mustapha A, Ahmad MH
    ScientificWorldJournal, 2014;2014:813983.
    PMID: 24587757 DOI: 10.1155/2014/813983
    The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality.
    Matched MeSH terms: Information Storage and Retrieval/methods*
  4. Fulltext DAD-match; Security technique to prevent denial of service attack on duplicate address detection process in IPv6 link-local network
    Al-Ani AK, Anbar M, Manickam S, Al-Ani A
    PLoS One, 2019;14(4):e0214518.
    PMID: 30939154 DOI: 10.1371/journal.pone.0214518
    An efficiently unlimited address space is provided by Internet Protocol version 6 (IPv6). It aims to accommodate thousands of hundreds of unique devices on a similar link. This can be achieved through the Duplicate Address Detection (DAD) process. It is considered one of the core IPv6 network's functions. It is implemented to make sure that IP addresses do not conflict with each other on the same link. However, IPv6 design's functions are exposed to security threats like the DAD process, which is vulnerable to Denial of Service (DoS) attack. Such a threat prevents the host from configuring its IP address by responding to each Neighbor Solicitation (NS) through fake Neighbor Advertisement (NA). Various mechanisms have been proposed to secure the IPv6 DAD procedure. The proposed mechanisms, however, suffer from complexity, high processing time, and the consumption of more resources. The experiments-based findings revealed that all the existing mechanisms had failed to secure the IPv6 DAD process. Therefore, DAD-match security technique is proposed in this study to efficiently secure the DAD process consuming less processing time. DAD-match is built based on SHA-3 to hide the exchange tentative IP among hosts throughout the process of DAD in an IPv6 link-local network. The obtained experimental results demonstrated that the DAD-match security technique achieved less processing time compared with the existing mechanisms as it can resist a range of different threats like collision and brute-force attacks. The findings concluded that the DAD-match technique effectively prevents the DoS attack during the DAD process. The DAD-match technique is implemented on a small area IPv6 network; hence, the author future work is to implement and test the DAD-match technique on a large area IPv6 network.
    Matched MeSH terms: Information Storage and Retrieval/methods
  5. Fulltext Classification of forensic autopsy reports through conceptual graph-based document representation model
    Mujtaba G, Shuib L, Raj RG, Rajandram R, Shaikh K, Al-Garadi MA
    J Biomed Inform, 2018 06;82:88-105.
    PMID: 29738820 DOI: 10.1016/j.jbi.2018.04.013
    Text categorization has been used extensively in recent years to classify plain-text clinical reports. This study employs text categorization techniques for the classification of open narrative forensic autopsy reports. One of the key steps in text classification is document representation. In document representation, a clinical report is transformed into a format that is suitable for classification. The traditional document representation technique for text categorization is the bag-of-words (BoW) technique. In this study, the traditional BoW technique is ineffective in classifying forensic autopsy reports because it merely extracts frequent but discriminative features from clinical reports. Moreover, this technique fails to capture word inversion, as well as word-level synonymy and polysemy, when classifying autopsy reports. Hence, the BoW technique suffers from low accuracy and low robustness unless it is improved with contextual and application-specific information. To overcome the aforementioned limitations of the BoW technique, this research aims to develop an effective conceptual graph-based document representation (CGDR) technique to classify 1500 forensic autopsy reports from four (4) manners of death (MoD) and sixteen (16) causes of death (CoD). Term-based and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT) based conceptual features were extracted and represented through graphs. These features were then used to train a two-level text classifier. The first level classifier was responsible for predicting MoD. In addition, the second level classifier was responsible for predicting CoD using the proposed conceptual graph-based document representation technique. To demonstrate the significance of the proposed technique, its results were compared with those of six (6) state-of-the-art document representation techniques. Lastly, this study compared the effects of one-level classification and two-level classification on the experimental results. The experimental results indicated that the CGDR technique achieved 12% to 15% improvement in accuracy compared with fully automated document representation baseline techniques. Moreover, two-level classification obtained better results compared with one-level classification. The promising results of the proposed conceptual graph-based document representation technique suggest that pathologists can adopt the proposed system as their basis for second opinion, thereby supporting them in effectively determining CoD.
    Matched MeSH terms: Information Storage and Retrieval
  6. Fulltext House building tips (HBT) corpus dataset as a resource to discover Malay architectural ingenuity and identity
    Zaini MF, Sarudin A, Muhammad MM, Osman Z, Mohamed Redzwan HF, Al-Muhsin MA
    Data Brief, 2021 Jun;36:107013.
    PMID: 33898671 DOI: 10.1016/j.dib.2021.107013
    House Building Tips is the title of a classic text containing historical information on early house construction in Malay communities. These tips were written by a scholar with knowledge of house construction through observation of the surrounding environment. In Malaysia, written sources or records of house construction are scarce and underexposed. As such, this research was conducted to guarantee the written legacy of the construction of Malay houses. The purpose of this paper is to introduce a statistical data source of house building tips that is laden with Malay ingenuity and identity. The wordlists generated from this study can become a source of reference for the field of Malay architecture. Accordingly, this study utilises the quantitative method by applying the Linguistic Corpus Statistical Approach; these data utilise specific corpus development procedures, beginning with text collection, scanning and cleaning processes, text annotation, and data storing in plain text. Next, the data analysis procedure utilises a corpus software, LancsBox, to generate specialised wordlists. The bubble graphs are developed based on these wordlists through the Tableau software, and illustrate the most used lexical items with the raw and relative frequency values. This facilitates searches for, and the reading of, architectural words and architectural word references. These data represent written sources that need to be preserved and become points of reference concerning Malay architectural ingenuity and identity.
    Matched MeSH terms: Information Storage and Retrieval
  7. Fulltext Trusted computing strengthens cloud authentication
    Ghazizadeh E, Zamani M, Ab Manan JL, Alizadeh M
    ScientificWorldJournal, 2014;2014:260187.
    PMID: 24701149 DOI: 10.1155/2014/260187
    Cloud computing is a new generation of technology which is designed to provide the commercial necessities, solve the IT management issues, and run the appropriate applications. Another entry on the list of cloud functions which has been handled internally is Identity Access Management (IAM). Companies encounter IAM as security challenges while adopting more technologies became apparent. Trust Multi-tenancy and trusted computing based on a Trusted Platform Module (TPM) are great technologies for solving the trust and security concerns in the cloud identity environment. Single sign-on (SSO) and OpenID have been released to solve security and privacy problems for cloud identity. This paper proposes the use of trusted computing, Federated Identity Management, and OpenID Web SSO to solve identity theft in the cloud. Besides, this proposed model has been simulated in .Net environment. Security analyzing, simulation, and BLP confidential model are three ways to evaluate and analyze our proposed model.
    Matched MeSH terms: Information Storage and Retrieval/standards; Information Storage and Retrieval/trends
  8. Fulltext An innovative privacy preserving technique for incremental datasets on cloud computing
    Aldeen YA, Salleh M, Aljeroudi Y
    J Biomed Inform, 2016 08;62:107-16.
    PMID: 27369566 DOI: 10.1016/j.jbi.2016.06.011
    Cloud computing (CC) is a magnificent service-based delivery with gigantic computer processing power and data storage across connected communications channels. It imparted overwhelming technological impetus in the internet (web) mediated IT industry, where users can easily share private data for further analysis and mining. Furthermore, user affable CC services enable to deploy sundry applications economically. Meanwhile, simple data sharing impelled various phishing attacks and malware assisted security threats. Some privacy sensitive applications like health services on cloud that are built with several economic and operational benefits necessitate enhanced security. Thus, absolute cyberspace security and mitigation against phishing blitz became mandatory to protect overall data privacy. Typically, diverse applications datasets are anonymized with better privacy to owners without providing all secrecy requirements to the newly added records. Some proposed techniques emphasized this issue by re-anonymizing the datasets from the scratch. The utmost privacy protection over incremental datasets on CC is far from being achieved. Certainly, the distribution of huge datasets volume across multiple storage nodes limits the privacy preservation. In this view, we propose a new anonymization technique to attain better privacy protection with high data utility over distributed and incremental datasets on CC. The proficiency of data privacy preservation and improved confidentiality requirements is demonstrated through performance evaluation.
    Matched MeSH terms: Information Storage and Retrieval*
  9. Fulltext Towards dynamic remote data auditing in computational clouds
    Sookhak M, Akhunzada A, Gani A, Khurram Khan M, Anuar NB
    ScientificWorldJournal, 2014;2014:269357.
    PMID: 25121114 DOI: 10.1155/2014/269357
    Cloud computing is a significant shift of computational paradigm where computing as a utility and storing data remotely have a great potential. Enterprise and businesses are now more interested in outsourcing their data to the cloud to lessen the burden of local data storage and maintenance. However, the outsourced data and the computation outcomes are not continuously trustworthy due to the lack of control and physical possession of the data owners. To better streamline this issue, researchers have now focused on designing remote data auditing (RDA) techniques. The majority of these techniques, however, are only applicable for static archive data and are not subject to audit the dynamically updated outsourced data. We propose an effectual RDA technique based on algebraic signature properties for cloud storage system and also present a new data structure capable of efficiently supporting dynamic data operations like append, insert, modify, and delete. Moreover, this data structure empowers our method to be applicable for large-scale data with minimum computation cost. The comparative analysis with the state-of-the-art RDA schemes shows that the proposed scheme is secure and highly efficient in terms of the computation and communication overhead on the auditor and server.
    Matched MeSH terms: Information Storage and Retrieval/methods*
  10. Clinical evaluation of watermarked medical images
    Zain JM, Fauzi AM, Aziz AA
    Conf Proc IEEE Eng Med Biol Soc, 2007 10 20;2006:5459-62.
    PMID: 17946306
    Digital watermarking medical images provides security to the images. The purpose of this study was to see whether digitally watermarked images changed clinical diagnoses when assessed by radiologists. We embedded 256 bits watermark to various medical images in the region of non-interest (RONI) and 480K bits in both region of interest (ROI) and RONI. Our results showed that watermarking medical images did not alter clinical diagnoses. In addition, there was no difference in image quality when visually assessed by the medical radiologists. We therefore concluded that digital watermarking medical images were safe in terms of preserving image quality for clinical purposes.
    Matched MeSH terms: Information Storage and Retrieval
  11. MIARS: a medical image retrieval system
    Mueen A, Zainuddin R, Baba MS
    J Med Syst, 2010 Oct;34(5):859-64.
    PMID: 20703623 DOI: 10.1007/s10916-009-9300-y
    The next generation of medical information system will integrate multimedia data to assist physicians in clinical decision-making, diagnoses, teaching, and research. This paper describes MIARS (Medical Image Annotation and Retrieval System). MIARS not only provides automatic annotation, but also supports text based as well as image based retrieval strategies, which play important roles in medical training, research, and diagnostics. The system utilizes three trained classifiers, which are trained using training images. The goal of these classifiers is to provide multi-level automatic annotation. Another main purpose of the MIARS system is to study image semantic retrieval strategy by which images can be retrieved according to different levels of annotation.
    Matched MeSH terms: Information Storage and Retrieval*
  12. Automatic multilevel medical image annotation and retrieval
    Mueen A, Zainuddin R, Baba MS
    J Digit Imaging, 2008 Sep;21(3):290-5.
    PMID: 17846834
    Image retrieval at the semantic level mostly depends on image annotation or image classification. Image annotation performance largely depends on three issues: (1) automatic image feature extraction; (2) a semantic image concept modeling; (3) algorithm for semantic image annotation. To address first issue, multilevel features are extracted to construct the feature vector, which represents the contents of the image. To address second issue, domain-dependent concept hierarchy is constructed for interpretation of image semantic concepts. To address third issue, automatic multilevel code generation is proposed for image classification and multilevel image annotation. We make use of the existing image annotation to address second and third issues. Our experiments on a specific domain of X-ray images have given encouraging results.
    Matched MeSH terms: Information Storage and Retrieval/methods*
  13. The economic burden of schizophrenia in Malaysia
    Teoh SL, Chong HY, Abdul Aziz S, Chemi N, Othman AR, Md Zaki N, et al.
    Neuropsychiatr Dis Treat, 2017;13:1979-1987.
    PMID: 28814869 DOI: 10.2147/NDT.S137140
    INTRODUCTION: Schizophrenia (SCZ) is a highly debilitating disease despite its low prevalence. The economic burden associated with SCZ is substantial and mainly attributed to productivity loss. To improve the understanding of economic burden of SCZ in the low- and middle-income country regions, we aimed to determine the economic burden of SCZ in Malaysia.

    METHODS: A retrospective study was conducted using a prevalence-based approach from a societal perspective in Malaysia with a 1 year period from 2013. We used micro-costing technique with bottom-up method and included direct medical cost, direct non-medical cost, and indirect cost. The main data source was medical chart review which was conducted in Hospital Kuala Lumpur (HKL). The medical charts were identified electronically by matching the unique patient's identification number registered under the National Mental Health Schizophrenia Registry and the list of patients in HKL in 2013. Other data sources were government documents, literatures, and local websites. To ensure robustness of result, probabilistic sensitivity analysis was conducted.

    RESULTS: The total estimated number of treated SCZ cases in Malaysia in 2015 was 15,104 with the total economic burden of USD 100 million (M) which was equivalent to 0.04% of the national gross domestic product. On average, the mean cost per patient was USD 6,594. Of the total economic burden of SCZ, 72% was attributed to indirect cost, costing at USD 72M, followed by direct medical cost (26%), costing at USD 26M, and direct non-medical cost (2%), costing at USD 1.7M.

    CONCLUSION: This study highlights the magnitude of economic burden of SCZ and informs the policy-makers that there is an inadequate support for SCZ patients. More resources should be allocated to improve the condition of SCZ patients and to reduce the economic burden.

    Matched MeSH terms: Information Storage and Retrieval
  14. Multiresolution wavelet analysis for efficient analysis, compression and remote display of long-term physiological signals
    Khuan LY, Bister M, Blanchfield P, Salleh YM, Ali RA, Chan TH
    Australas Phys Eng Sci Med, 2006 Jun;29(2):216-28.
    PMID: 16845928
    Increased inter-equipment connectivity coupled with advances in Web technology allows ever escalating amounts of physiological data to be produced, far too much to be displayed adequately on a single computer screen. The consequence is that large quantities of insignificant data will be transmitted and reviewed. This carries an increased risk of overlooking vitally important transients. This paper describes a technique to provide an integrated solution based on a single algorithm for the efficient analysis, compression and remote display of long-term physiological signals with infrequent short duration, yet vital events, to effect a reduction in data transmission and display cluttering and to facilitate reliable data interpretation. The algorithm analyses data at the server end and flags significant events. It produces a compressed version of the signal at a lower resolution that can be satisfactorily viewed in a single screen width. This reduced set of data is initially transmitted together with a set of 'flags' indicating where significant events occur. Subsequent transmissions need only involve transmission of flagged data segments of interest at the required resolution. Efficient processing and code protection with decomposition alone is novel. The fixed transmission length method ensures clutter-less display, irrespective of the data length. The flagging of annotated events in arterial oxygen saturation, electroencephalogram and electrocardiogram illustrates the generic property of the algorithm. Data reduction of 87% to 99% and improved displays are demonstrated.
    Matched MeSH terms: Information Storage and Retrieval/methods*
  15. Forensic Significance of Gunshot Impact Marks on Inanimate Objects: The Need for Translational Research
    Nordin FA, Bominathan UR, Abdullah AFL, Chang KH
    J Forensic Sci, 2020 Jan;65(1):11-25.
    PMID: 31373699 DOI: 10.1111/1556-4029.14142
    Wherever an impact mark is found, either on the surface or on the recovered projectile, it is important for forensic investigators to extract useful information in solving shooting-related cases. This article reviews a collection of works on examination of impact marks upon striking of projectiles on inanimate objects, emphasizing on the retrievable information from a shooting scene and their forensic significance in shooting event reconstruction. Literature suggested that impact marks on target surfaces and the degree of deformation on striking projectiles vary according to different combinations of ammunition and surface materials. It was noted that conditions in real-case scenarios further differed unpredictably in comparison with controlled studies, where forensic investigation should be treated as case-specific basis. Furthermore, the way forensic science is researched and applied operationally has to be reconsidered to reduce the gap via translational approach for more effective use of forensic evidence.
    Matched MeSH terms: Information Storage and Retrieval
  16. Fulltext Altmetrics: A new paradigm for scholarly communication
    Shekhawat KS, Chauhan A
    Indian J Dent Res, 2019 3 23;30(1):125-126.
    PMID: 30900670 DOI: 10.4103/ijdr.IJDR_27_17
    Counting citations have been the usual norm to determine the impact of any research and/or scholar. However, with majority of the scholarly activities happening on the World Wide Web, traditional counting of citations is now being termed "slower." The recent explosion of online data storage for many articles may serve as a pool which uses social media sites to navigate. Altmetrics has been proposed as the new entity which aims to change the focus of the scholarly reward system to value and encourage web-native scholarship. This paper makes an attempt to understand altmetrics.
    Matched MeSH terms: Information Storage and Retrieval
  17. A novel strategy for load balancing of distributed medical applications
    Logeswaran R, Chen LC
    J Med Syst, 2012 Apr;36(2):483-90.
    PMID: 20703702 DOI: 10.1007/s10916-010-9493-0
    Current trends in medicine, specifically in the electronic handling of medical applications, ranging from digital imaging, paperless hospital administration and electronic medical records, telemedicine, to computer-aided diagnosis, creates a burden on the network. Distributed Service Architectures, such as Intelligent Network (IN), Telecommunication Information Networking Architecture (TINA) and Open Service Access (OSA), are able to meet this new challenge. Distribution enables computational tasks to be spread among multiple processors; hence, performance is an important issue. This paper proposes a novel approach in load balancing, the Random Sender Initiated Algorithm, for distribution of tasks among several nodes sharing the same computational object (CO) instances in Distributed Service Architectures. Simulations illustrate that the proposed algorithm produces better network performance than the benchmark load balancing algorithms-the Random Node Selection Algorithm and the Shortest Queue Algorithm, especially under medium and heavily loaded conditions.
    Matched MeSH terms: Information Storage and Retrieval
  18. Fulltext Context modelling for just-in-time mobile information retrieval (JIT-MobIR)
    Az Azrinudin Alidin, Crestani, Fabio
    Pertanika Journal of Science & Technology, 2013;21(1):227-238.
    MyJurnal
    Mobile users have the capability of accessing information anywhere at any time with the introduction of mobile browsers and mobile web search. However, the current mobile browsers are implemented without considering the characteristics of mobile searches. As a result, mobile users need to devote time and effort in order to retrieve relevant information from the web in mobile devices. On the other hand, mobile users often request information related to their surroundings, which is also known as context. This recognizes the importance of including context in information retrieval. Besides, the availability of the embedded sensors in mobile devices has supported the recognition of context. In this study, the context acquisition and utilization for mobile information retrieval are proposed. The “just-in-time” approach is exploited in which the information that is relevant to a user is retrieved without the user requesting it. This will reduce the mobile user’s effort, time and interaction when retrieving information in mobile devices. In this paper, the context dimensions and context model are presented. Simple experiments are shown where user context is predicted using the context model.
    Matched MeSH terms: Information Storage and Retrieval
  19. Fulltext Development of electronic medical records for clinical and research purposes: the breast cancer module using an implementation framework in a middle income country- Malaysia
    Mohd Nor NA, Taib NA, Saad M, Zaini HS, Ahmad Z, Ahmad Y, et al.
    BMC Bioinformatics, 2019 Feb 04;19(Suppl 13):402.
    PMID: 30717675 DOI: 10.1186/s12859-018-2406-9
    BACKGROUND: Advances in medical domain has led to an increase of clinical data production which offers enhancement opportunities for clinical research sector. In this paper, we propose to expand the scope of Electronic Medical Records in the University Malaya Medical Center (UMMC) using different techniques in establishing interoperability functions between multiple clinical departments involving diagnosis, screening and treatment of breast cancer and building automatic systems for clinical audits as well as for potential data mining to enhance clinical breast cancer research in the future.

    RESULTS: Quality Implementation Framework (QIF) was adopted to develop the breast cancer module as part of the in-house EMR system used at UMMC, called i-Pesakit©. The completion of the i-Pesakit© Breast Cancer Module requires management of clinical data electronically, integration of clinical data from multiple internal clinical departments towards setting up of a research focused patient data governance model. The 14 QIF steps were performed in four main phases involved in this study which are (i) initial considerations regarding host setting, (ii) creating structure for implementation, (iii) ongoing structure once implementation begins, and (iv) improving future applications. The architectural framework of the module incorporates both clinical and research needs that comply to the Personal Data Protection Act.

    CONCLUSION: The completion of the UMMC i-Pesakit© Breast Cancer Module required populating EMR including management of clinical data access, establishing information technology and research focused governance model and integrating clinical data from multiple internal clinical departments. This multidisciplinary collaboration has enhanced the quality of data capture in clinical service, benefited hospital data monitoring, quality assurance, audit reporting and research data management, as well as a framework for implementing a responsive EMR for a clinical and research organization in a typical middle-income country setting. Future applications include establishing integration with external organization such as the National Registration Department for mortality data, reporting of institutional data for national cancer registry as well as data mining for clinical research. We believe that integration of multiple clinical visit data sources provides a more comprehensive, accurate and real-time update of clinical data to be used for epidemiological studies and audits.

    Matched MeSH terms: Information Storage and Retrieval
  20. Fish Ontology framework for taxonomy-based fish recognition
    Ali NM, Khan HA, Then AY, Ving Ching C, Gaur M, Dhillon SK
    PeerJ, 2017;5:e3811.
    PMID: 28929028 DOI: 10.7717/peerj.3811
    Life science ontologies play an important role in Semantic Web. Given the diversity in fish species and the associated wealth of information, it is imperative to develop an ontology capable of linking and integrating this information in an automated fashion. As such, we introduce the Fish Ontology (FO), an automated classification architecture of existing fish taxa which provides taxonomic information on unknown fish based on metadata restrictions. It is designed to support knowledge discovery, provide semantic annotation of fish and fisheries resources, data integration, and information retrieval. Automated classification for unknown specimens is a unique feature that currently does not appear to exist in other known ontologies. Examples of automated classification for major groups of fish are demonstrated, showing the inferred information by introducing several restrictions at the species or specimen level. The current version of FO has 1,830 classes, includes widely used fisheries terminology, and models major aspects of fish taxonomy, grouping, and character. With more than 30,000 known fish species globally, the FO will be an indispensable tool for fish scientists and other interested users.
    Matched MeSH terms: Information Storage and Retrieval
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