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Fulltext YouTube Video Comments on Healthy Eating: Descriptive and Predictive Analysis

Teng S, Khong KW, Pahlevan Sharif S, Ahmed A

JMIR Public Health Surveill, 2020 10 01;6(4):e19618.
PMID: 33001036 DOI: 10.2196/19618

BACKGROUND: Poor nutrition and food selection lead to health issues such as obesity, cardiovascular disease, diabetes, and cancer. This study of YouTube comments aims to uncover patterns of food choices and the factors driving them, in addition to exploring the sentiments of healthy eating in networked communities.
OBJECTIVE: The objectives of the study are to explore the determinants, motives, and barriers to healthy eating behaviors in online communities and provide insight into YouTube video commenters' perceptions and sentiments of healthy eating through text mining techniques.
METHODS: This paper applied text mining techniques to identify and categorize meaningful healthy eating determinants. These determinants were then incorporated into hypothetically defined constructs that reflect their thematic and sentimental nature in order to test our proposed model using a variance-based structural equation modeling procedure.
RESULTS: With a dataset of 4654 comments extracted from YouTube videos in the context of Malaysia, we apply a text mining method to analyze the perceptions and behavior of healthy eating. There were 10 clusters identified with regard to food ingredients, food price, food choice, food portion, well-being, cooking, and culture in the concept of healthy eating. The structural equation modeling results show that clusters are positively associated with healthy eating with all P values less than .001, indicating a statistical significance of the study results. People hold complex and multifaceted beliefs about healthy eating in the context of YouTube videos. Fruits and vegetables are the epitome of healthy foods. Despite having a favorable perception of healthy eating, people may not purchase commonly recognized healthy food if it has a premium price. People associate healthy eating with weight concerns. Food taste, variety, and availability are identified as reasons why Malaysians cannot act on eating healthily.
CONCLUSIONS: This study offers significant value to the existing literature of health-related studies by investigating the rich and diverse social media data gleaned from YouTube. This research integrated text mining analytics with predictive modeling techniques to identify thematic constructs and analyze the sentiments of healthy eating.

Matched MeSH terms: Data Mining
Visualizing disaster attitudes resulting from terrorist activities

Khalid HM, Helander MG, Hood NA

Appl Ergon, 2013 Sep;44(5):671-9.
PMID: 22944486 DOI: 10.1016/j.apergo.2012.06.005

The purpose of this study was to analyze people's attitudes to disasters by investigating how people feel, behave and think during disasters. We focused on disasters induced by humans, such as terrorist attacks. Two types of textual information were collected - from Internet blogs and from research papers. The analysis enabled forecasting of attitudes for the design of proactive disaster advisory scheme. Text was analyzed using a text mining tool, Leximancer. The outcome of this analysis revealed core themes and concepts in the text concerning people's attitudes. The themes and concepts were sorted into three broad categories: Affect, Behaviour, and Cognition (ABC), and the data was visualized in semantic maps. The maps reveal several knowledge pathways of ABC for developing attitudinal ontologies, which describe the relations between affect, behaviour and cognition, and the sequence in which they develop. Clearly, terrorist attacks induced trauma and people became highly vulnerable.

Matched MeSH terms: Data Mining
Using fuzzy association rule mining in cancer classification

Mahmoodian H, Hamiruce Marhaban M, Abdulrahim R, Rosli R, Saripan I

Australas Phys Eng Sci Med, 2011 Apr;34(1):41-54.
PMID: 21327594 DOI: 10.1007/s13246-011-0054-8

The classification of the cancer tumors based on gene expression profiles has been extensively studied in numbers of studies. A wide variety of cancer datasets have been implemented by the various methods of gene selection and classification to identify the behavior of the genes in tumors and find the relationships between them and outcome of diseases. Interpretability of the model, which is developed by fuzzy rules and linguistic variables in this study, has been rarely considered. In addition, creating a fuzzy classifier with high performance in classification that uses a subset of significant genes which have been selected by different types of gene selection methods is another goal of this study. A new algorithm has been developed to identify the fuzzy rules and significant genes based on fuzzy association rule mining. At first, different subset of genes which have been selected by different methods, were used to generate primary fuzzy classifiers separately and then proposed algorithm was implemented to mix the genes which have been associated in the primary classifiers and generate a new classifier. The results show that fuzzy classifier can classify the tumors with high performance while presenting the relationships between the genes by linguistic variables.

Matched MeSH terms: Data Mining/methods
Fulltext Unsupervised Network Analysis of the Plastic Supraoptic Nucleus Transcriptome Predicts Caprin2 Regulatory Interactions

Loh SY, Jahans-Price T, Greenwood MP, Greenwood M, Hoe SZ, Konopacka A, et al.

eNeuro, 2017 12 21;4(6).
PMID: 29279858 DOI: 10.1523/ENEURO.0243-17.2017

The supraoptic nucleus (SON) is a group of neurons in the hypothalamus responsible for the synthesis and secretion of the peptide hormones vasopressin and oxytocin. Following physiological cues, such as dehydration, salt-loading and lactation, the SON undergoes a function related plasticity that we have previously described in the rat at the transcriptome level. Using the unsupervised graphical lasso (Glasso) algorithm, we reconstructed a putative network from 500 plastic SON genes in which genes are the nodes and the edges are the inferred interactions. The most active nodal gene identified within the network was Caprin2. Caprin2 encodes an RNA-binding protein that we have previously shown to be vital for the functioning of osmoregulatory neuroendocrine neurons in the SON of the rat hypothalamus. To test the validity of the Glasso network, we either overexpressed or knocked down Caprin2 transcripts in differentiated rat pheochromocytoma PC12 cells and showed that these manipulations had significant opposite effects on the levels of putative target mRNAs. These studies suggest that the predicative power of the Glasso algorithm within an in vivo system is accurate, and identifies biological targets that may be important to the functional plasticity of the SON.

Matched MeSH terms: Data Mining
The Extended Polydimensional Immunome Characterization (EPIC) web-based reference and discovery tool for cytometry data

Yeo JG, Wasser M, Kumar P, Pan L, Poh SL, Ally F, et al.

Nat Biotechnol, 2020 06;38(6):679-684.
PMID: 32440006 DOI: 10.1038/s41587-020-0532-1
Matched MeSH terms: Data Mining
Fulltext Text mining in mosquito-borne disease: A systematic review

Ong SQ, Pauzi MBM, Gan KH

Acta Trop, 2022 Jul;231:106447.
PMID: 35430265 DOI: 10.1016/j.actatropica.2022.106447

Mosquito-borne diseases are emerging and re-emerging across the globe, especially after the COVID19 pandemic. The recent advances in text mining in infectious diseases hold the potential of providing timely access to explicit and implicit associations among information in the text. In the past few years, the availability of online text data in the form of unstructured or semi-structured text with rich content of information from this domain enables many studies to provide solutions in this area, e.g., disease-related knowledge discovery, disease surveillance, early detection system, etc. However, a recent review of text mining in the domain of mosquito-borne disease was not available to the best of our knowledge. In this review, we survey the recent works in the text mining techniques used in combating mosquito-borne diseases. We highlight the corpus sources, technologies, applications, and the challenges faced by the studies, followed by the possible future directions that can be taken further in this domain. We present a bibliometric analysis of the 294 scientific articles that have been published in Scopus and PubMed in the domain of text mining in mosquito-borne diseases, from the year 2016 to 2021. The papers were further filtered and reviewed based on the techniques used to analyze the text related to mosquito-borne diseases. Based on the corpus of 158 selected articles, we found 27 of the articles were relevant and used text mining in mosquito-borne diseases. These articles covered the majority of Zika (38.70%), Dengue (32.26%), and Malaria (29.03%), with extremely low numbers or none of the other crucial mosquito-borne diseases like chikungunya, yellow fever, West Nile fever. Twitter was the dominant corpus resource to perform text mining in mosquito-borne diseases, followed by PubMed and LexisNexis databases. Sentiment analysis was the most popular technique of text mining to understand the discourse of the disease and followed by information extraction, which dependency relation and co-occurrence-based approach to extract relations and events. Surveillance was the main usage of most of the reviewed studies and followed by treatment, which focused on the drug-disease or symptom-disease association. The advance in text mining could improve the management of mosquito-borne diseases. However, the technique and application posed many limitations and challenges, including biases like user authentication and language, real-world implementation, etc. We discussed the future direction which can be useful to expand this area and domain. This review paper contributes mainly as a library for text mining in mosquito-borne diseases and could further explore the system for other neglected diseases.

Matched MeSH terms: Data Mining
Suicidal behaviour prediction models using machine learning techniques: A systematic review

Nordin N, Zainol Z, Mohd Noor MH, Chan LF

Artif Intell Med, 2022 10;132:102395.
PMID: 36207078 DOI: 10.1016/j.artmed.2022.102395

BACKGROUND: Early detection and prediction of suicidal behaviour are key factors in suicide control. In conjunction with recent advances in the field of artificial intelligence, there is increasing research into how machine learning can assist in the detection, prediction and treatment of suicidal behaviour. Therefore, this study aims to provide a comprehensive review of the literature exploring machine learning techniques in the study of suicidal behaviour prediction.
METHODS: A search of four databases was conducted: Web of Science, PubMed, Dimensions, and Scopus for research papers dated between January 2016 and September 2021. The search keywords are 'data mining', 'machine learning' in combination with 'suicidal behaviour', 'suicide', 'suicide attempt', 'suicidal ideation', 'suicide plan' and 'self-harm'. The studies that used machine learning techniques were synthesized according to the countries of the articles, sample description, sample size, classification tasks, number of features used to develop the models, types of machine learning techniques, and evaluation of performance metrics.
RESULTS: Thirty-five empirical articles met the criteria to be included in the current review. We provide a general overview of machine learning techniques, examine the feature categories, describe methodological challenges, and suggest areas for improvement and research directions. Ensemble prediction models have been shown to be more accurate and useful than single prediction models.
CONCLUSIONS: Machine learning has great potential for improving estimates of future suicidal behaviour and monitoring changes in risk over time. Further research can address important challenges and potential opportunities that may contribute to significant advances in suicide prediction.

Matched MeSH terms: Data Mining
Fulltext Statistical estimators as an alternative to standard deviation in weighted euclidean distance cluster analysis

Dalatu, Paul Inuwa, Habshah Midi

Pertanika Journal of Science & Technology, 2018;26(4):1823-1836.
MyJurnal

Clustering is basically one of the major sources of primary data mining tools. It makes
researchers understand the natural grouping of attributes in datasets. Clustering is an
unsupervised classification method with the major aim of partitioning, where objects in the
same cluster are similar, and objects which belong to different clusters vary significantly,
with respect to their attributes. However, the classical Standardized Euclidean distance,
which uses standard deviation to down weight maximum points of the ith features on the
distance clusters, has been criticized by many scholars that the method produces outliers,
lack robustness, and has 0% breakdown points. It also has low efficiency in normal
distribution. Therefore, to remedy the problem, we suggest two statistical estimators
which have 50% breakdown points namely the Sn and Qn estimators, with 58% and 82%
efficiency, respectively. The proposed methods evidently outperformed the existing methods
in down weighting the maximum points of the ith features in distance-based clustering
analysis.

Matched MeSH terms: Data Mining
Fulltext Role of biological Data Mining and Machine Learning Techniques in Detecting and Diagnosing the Novel Coronavirus (COVID-19): A Systematic Review

Albahri AS, Hamid RA, Alwan JK, Al-Qays ZT, Zaidan AA, Zaidan BB, et al.

J Med Syst, 2020 May 25;44(7):122.
PMID: 32451808 DOI: 10.1007/s10916-020-01582-x

Coronaviruses (CoVs) are a large family of viruses that are common in many animal species, including camels, cattle, cats and bats. Animal CoVs, such as Middle East respiratory syndrome-CoV, severe acute respiratory syndrome (SARS)-CoV, and the new virus named SARS-CoV-2, rarely infect and spread among humans. On January 30, 2020, the International Health Regulations Emergency Committee of the World Health Organisation declared the outbreak of the resulting disease from this new CoV called 'COVID-19', as a 'public health emergency of international concern'. This global pandemic has affected almost the whole planet and caused the death of more than 315,131 patients as of the date of this article. In this context, publishers, journals and researchers are urged to research different domains and stop the spread of this deadly virus. The increasing interest in developing artificial intelligence (AI) applications has addressed several medical problems. However, such applications remain insufficient given the high potential threat posed by this virus to global public health. This systematic review addresses automated AI applications based on data mining and machine learning (ML) algorithms for detecting and diagnosing COVID-19. We aimed to obtain an overview of this critical virus, address the limitations of utilising data mining and ML algorithms, and provide the health sector with the benefits of this technique. We used five databases, namely, IEEE Xplore, Web of Science, PubMed, ScienceDirect and Scopus and performed three sequences of search queries between 2010 and 2020. Accurate exclusion criteria and selection strategy were applied to screen the obtained 1305 articles. Only eight articles were fully evaluated and included in this review, and this number only emphasised the insufficiency of research in this important area. After analysing all included studies, the results were distributed following the year of publication and the commonly used data mining and ML algorithms. The results found in all papers were discussed to find the gaps in all reviewed papers. Characteristics, such as motivations, challenges, limitations, recommendations, case studies, and features and classes used, were analysed in detail. This study reviewed the state-of-the-art techniques for CoV prediction algorithms based on data mining and ML assessment. The reliability and acceptability of extracted information and datasets from implemented technologies in the literature were considered. Findings showed that researchers must proceed with insights they gain, focus on identifying solutions for CoV problems, and introduce new improvements. The growing emphasis on data mining and ML techniques in medical fields can provide the right environment for change and improvement.

Matched MeSH terms: Data Mining/methods*
Fulltext Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset

M. Hafiz Fazren Abd Rahman, Wan Wardatul Amani Wan Salim, M. Firdaus Abd-Wahab

Biological and Natural Resources Engineering Journal, 2020;3(1):48-61.
MyJurnal

The steep rise of cases pertaining to Diabetes Mellitus (DM) condition among global population has encouraged extensive researches on DM, which led to exhaustive accumulation of data related to DM. In this case, data mining and machine learning applications prove to be a powerful tool in transforming data into meaningful deductions. Several machine learning tools have shown great promise in diabetes classification. However, challenges remain in obtaining an accurate model suitable for real world application. Most disease risk-prediction modelling are found to be specific to a local population. Moreover, real-world data are likely to be complex, incomplete and unorganized, thus, convoluting efforts to develop models around it. This research aims to develop a robust prediction model for classification of type 2 diabetes mellitus (T2DM), with the interest of a Malaysian population, using three different machine learning algorithms; Decision Tree, Support Vector Machine and Naïve Bayes. Data pre-processing methods are utilised to the raw data to improve model performance. This study uses datasets obtained from the IIUM Medical Centre for classification and modelling. Ultimately, the performance of each model is validated, evaluated and compared based on several statistical metrics that measures accuracy, precision, sensitivity and efficiency. This study shows that the random forest model provides the best overall prediction performance in terms of accuracy (0.87), sensitivity (0.9), specificity (0.8), precision (0.9), F1-score (0.9) and AUC value (0.93) (Normal).

Matched MeSH terms: Data Mining
Fulltext Review of context-based similarity measure for categorical data

Nurul Adzlyana, M.S., Rosma, M.D., Nurazzah, A.R.

Pertanika Journal of Science & Technology, 2017;25(2):619-630.
MyJurnal

Data mining processes such as clustering, classification, regression and outlier detection are developed based on similarity between two objects. Data mining processes of categorical data is found to be most challenging. Earlier similarity measures are context-free. In recent years, researchers have come up with context-sensitive similarity measure based on the relationships of objects. This paper provides an in-depth review of context-based similarity measures. Descriptions of algorithm for four context-based similarity measure, namely Association-based similarity measure, DILCA, CBDL and the hybrid context-based similarity measure, are described. Advantages and limitations of each context-based similarity measure are identified and explained. Context-based similarity measure is highly recommended for data-mining tasks for categorical data. The findings of this paper will help data miners in choosing appropriate similarity measures to achieve more accurate classification or clustering results.

Matched MeSH terms: Data Mining
Fulltext Pooling and expanding registries of familial hypercholesterolaemia to assess gaps in care and improve disease management and outcomes: Rationale and design of the global EAS Familial Hypercholesterolaemia Studies Collaboration

EAS Familial Hypercholesterolaemia Studies Collaboration, Vallejo-Vaz AJ, Akram A, Kondapally Seshasai SR, Cole D, Watts GF, et al.

Atheroscler Suppl, 2016 Dec;22:1-32.
PMID: 27939304 DOI: 10.1016/j.atherosclerosissup.2016.10.001

The potential for global collaborations to better inform public health policy regarding major non-communicable diseases has been successfully demonstrated by several large-scale international consortia. However, the true public health impact of familial hypercholesterolaemia (FH), a common genetic disorder associated with premature cardiovascular disease, is yet to be reliably ascertained using similar approaches. The European Atherosclerosis Society FH Studies Collaboration (EAS FHSC) is a new initiative of international stakeholders which will help establish a global FH registry to generate large-scale, robust data on the burden of FH worldwide.

Matched MeSH terms: Data Mining
Fulltext Performance of SVM with multiple kernel learning for classification tasks of imbalanced datasets

Saeed, Sana, Ong, Hong Choon

Pertanika Journal of Science & Technology, 2019;27(1):527-545.
MyJurnal

Support vector machine (SVM) is one of the most popular algorithms in machine learning
and data mining. However, its reduced efficiency is usually observed for imbalanced
datasets. To improve the performance of SVM for binary imbalanced datasets, a new scheme
based on oversampling and the hybrid algorithm were introduced. Besides the use of a
single kernel function, SVM was applied with multiple kernel learning (MKL). A weighted
linear combination was defined based on the linear kernel function, radial basis function
(RBF kernel), and sigmoid kernel function for MKL. By generating the synthetic samples
in the minority class, searching the best choices of the SVM parameters and identifying
the weights of MKL by minimizing the objective function, the improved performance of
SVM was observed. To prove the strength of the proposed scheme, an experimental study,
including noisy borderline and real imbalanced datasets was conducted. SVM was applied
with linear kernel function, RBF kernel, sigmoid kernel function and MKL on all datasets.
The performance of SVM with all kernel functions was evaluated by using sensitivity,
G Mean, and F measure. A significantly improved performance of SVM with MKL was
observed by applying the proposed scheme.

Matched MeSH terms: Data Mining
Fulltext Order batching in warehouses by minimizing total tardiness: a hybrid approach of weighted association rule mining and genetic algorithms

Azadnia AH, Taheri S, Ghadimi P, Saman MZ, Wong KY

ScientificWorldJournal, 2013;2013:246578.
PMID: 23864823 DOI: 10.1155/2013/246578

One of the cost-intensive issues in managing warehouses is the order picking problem which deals with the retrieval of items from their storage locations in order to meet customer requests. Many solution approaches have been proposed in order to minimize traveling distance in the process of order picking. However, in practice, customer orders have to be completed by certain due dates in order to avoid tardiness which is neglected in most of the related scientific papers. Consequently, we proposed a novel solution approach in order to minimize tardiness which consists of four phases. First of all, weighted association rule mining has been used to calculate associations between orders with respect to their due date. Next, a batching model based on binary integer programming has been formulated to maximize the associations between orders within each batch. Subsequently, the order picking phase will come up which used a Genetic Algorithm integrated with the Traveling Salesman Problem in order to identify the most suitable travel path. Finally, the Genetic Algorithm has been applied for sequencing the constructed batches in order to minimize tardiness. Illustrative examples and comparisons are presented to demonstrate the proficiency and solution quality of the proposed approach.

Matched MeSH terms: Data Mining/methods*
Fulltext Natural language processing in narrative breast radiology reporting in University Malaya Medical Centre

Tan WM, Ng WL, Ganggayah MD, Hoe VCW, Rahmat K, Zaini HS, et al.

Health Informatics J, 2023;29(3):14604582231203763.
PMID: 37740904 DOI: 10.1177/14604582231203763

Radiology reporting is narrative, and its content depends on the clinician's ability to interpret the images accurately. A tertiary hospital, such as anonymous institute, focuses on writing reports narratively as part of training for medical personnel. Nevertheless, free-text reports make it inconvenient to extract information for clinical audits and data mining. Therefore, we aim to convert unstructured breast radiology reports into structured formats using natural language processing (NLP) algorithm. This study used 327 de-identified breast radiology reports from the anonymous institute. The radiologist identified the significant data elements to be extracted. Our NLP algorithm achieved 97% and 94.9% accuracy in training and testing data, respectively. Henceforth, the structured information was used to build the predictive model for predicting the value of the BIRADS category. The model based on random forest generated the highest accuracy of 92%. Our study not only fulfilled the demands of clinicians by enhancing communication between medical personnel, but it also demonstrated the usefulness of mineable structured data in yielding significant insights.

Matched MeSH terms: Data Mining
Natural Sirtuin Modulators in Drug Discovery: A Review (2010 -2020)

Chang Y, Yeong KY

Curr Med Chem, 2021 Mar 29.
PMID: 33781187 DOI: 10.2174/0929867328666210329124415

There have been intense research interests in sirtuins since the establishment of their regulatory roles in a myriad of pathological processes. In the last two decades, much research efforts have been dedicated to the development of sirtuin modulators. Although synthetic sirtuin modulators are the focus, natural modulators remain an integral part to be further explored in this area as they are found to possess therapeutic potential in various diseases including cancers, neurodegenerative diseases, and metabolic disorders. Owing to the importance of this cluster of compounds, this review gives a current stand on the naturally occurring sirtuin modulators, , associated molecular mechanisms and their therapeutic benefits.. Furthermore, comprehensive data mining resulted in detailed statistical data analyses pertaining to the development trend of sirtuin modulators from 2010-2020. Lastly, the challenges and future prospect of natural sirtuin modulators in drug discovery will also be discussed.

Matched MeSH terms: Data Mining
Fulltext Multi-population Black Hole Algorithm for the problem of data clustering

Salih SQ, Alsewari AA, Wahab HA, Mohammed MKA, Rashid TA, Das D, et al.

PLoS One, 2023;18(7):e0288044.
PMID: 37406006 DOI: 10.1371/journal.pone.0288044

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.

Matched MeSH terms: Data Mining/methods
Fulltext Modified multi-class classification using association rule mining

Yuhanis Yusof, Mohammed Hayel Refai

Pertanika Journal of Science & Technology, 2013;21(1):205-216.
MyJurnal

As the amount of document increases, automation of classification that aids the analysis and management of documents receive focal attention. Classification, based on association rules that are generated from a collection of documents, is a recent data mining approach that integrates association rule mining and classification. The existing approaches produces either high accuracy with large number of rules or a small number of association rules that generate low accuracy. This work presents an association rule mining that employs a new item production algorithm that generates a small number of rules and produces an acceptable accuracy rate. The proposed method is evaluated on UCI datasets and measured based on prediction accuracy and the number of generated association rules. Comparison is later made against an existing classifier, Multi-class Classification based on Association Rule (MCAR). From the undertaken experiments, it is learned that the proposed method produces similar accuracy rate as MCAR but yet uses lesser number of rules.

Matched MeSH terms: Data Mining
Modelling gully-erosion susceptibility in a semi-arid region, Iran: Investigation of applicability of certainty factor and maximum entropy models

Azareh A, Rahmati O, Rafiei-Sardooi E, Sankey JB, Lee S, Shahabi H, et al.

Sci Total Environ, 2019 Mar 10;655:684-696.
PMID: 30476849 DOI: 10.1016/j.scitotenv.2018.11.235

Gully erosion susceptibility mapping is a fundamental tool for land-use planning aimed at mitigating land degradation. However, the capabilities of some state-of-the-art data-mining models for developing accurate maps of gully erosion susceptibility have not yet been fully investigated. This study assessed and compared the performance of two different types of data-mining models for accurately mapping gully erosion susceptibility at a regional scale in Chavar, Ilam, Iran. The two methods evaluated were: Certainty Factor (CF), a bivariate statistical model; and Maximum Entropy (ME), an advanced machine learning model. Several geographic and environmental factors that can contribute to gully erosion were considered as predictor variables of gully erosion susceptibility. Based on an existing differential GPS survey inventory of gully erosion, a total of 63 eroded gullies were spatially randomly split in a 70:30 ratio for use in model calibration and validation, respectively. Accuracy assessments completed with the receiver operating characteristic curve method showed that the ME-based regional gully susceptibility map has an area under the curve (AUC) value of 88.6% whereas the CF-based map has an AUC of 81.8%. According to jackknife tests that were used to investigate the relative importance of predictor variables, aspect, distance to river, lithology and land use are the most influential factors for the spatial distribution of gully erosion susceptibility in this region of Iran. The gully erosion susceptibility maps produced in this study could be useful tools for land managers and engineers tasked with road development, urbanization and other future development.

Matched MeSH terms: Data Mining
Fulltext Mining personal data using smartphones and wearable devices: a survey

Habib ur Rehman M, Liew CS, Wah TY, Shuja J, Daghighi B

Sensors (Basel), 2015 Feb 13;15(2):4430-69.
PMID: 25688592 DOI: 10.3390/s150204430

The staggering growth in smartphone and wearable device use has led to a massive scale generation of personal (user-specific) data. To explore, analyze, and extract useful information and knowledge from the deluge of personal data, one has to leverage these devices as the data-mining platforms in ubiquitous, pervasive, and big data environments. This study presents the personal ecosystem where all computational resources, communication facilities, storage and knowledge management systems are available in user proximity. An extensive review on recent literature has been conducted and a detailed taxonomy is presented. The performance evaluation metrics and their empirical evidences are sorted out in this paper. Finally, we have highlighted some future research directions and potentially emerging application areas for personal data mining using smartphones and wearable devices.

Matched MeSH terms: Data Mining

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