Displaying publications 1 - 20 of 142 in total

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  1. Urban mining of obsolete computers by manual dismantling and waste printed circuit boards by chemical leaching and toxicity assessment of its waste residues
    Arya S, Patel A, Kumar S, Pau-Loke S
    Environ Pollut, 2021 Aug 15;283:117033.
    PMID: 33887669 DOI: 10.1016/j.envpol.2021.117033
    Waste residues and acidic effluents (post-processing of E-waste) released into the local surroundings cause perilous environmental threats and potential risks to human health. Only limited research and information are available toward the sustainable management of waste residues generated post resource recovery of E-waste components. In the present study, the manual processing of obsolete computer (keyboard, monitor, CPU, and mouse) and chemical leaching of waste printed circuit boards (WPCBs) (motherboard, hard drive, DVD drive, and power supply) were performed for urban mining. The toxicity characteristics of typical pollutants in the residues of the WPCBs (post chemical leaching) were studied by toxicity characteristics leaching procedure (TCLP) test. Manual dismantling techniques resulted in an efficient urban mining concept with an overall average profit estimation of INR 2513.73/US$ 34.59. The chemical leaching of WPCBs showed a high concentration of metal leaching like Cu (229662 ± 575.3 mg/kg) and Pb (36785.67 ± 13.07 mg/kg) in the motherboard after stripping epoxy coating. The toxicity test revealed that the concentration of Cu (245.746 ± 0.016 mg/l) in the treated waste residue and Cu (430.746 ± 0.0015 mg/l) and Pb (182.09 ± 0.0035 mg/l) in the non-treated waste residue exceeded the threshold limit. The concentrations of other elements As, Cd, Co, Cr, Ag, Mn, Zn, Ni, Fe, Se, and In were within the permissible limit. Hence, the waste residue stands non-hazardous except Cu and Pb. Stripping out the epoxy coating of WPCBs enhances the metal leaching concentrations. The study highlighted that efficient and appropriate E-waste urban mining has immense potential in tracing the waste scrap into secondary resources. This study also emphasized that the final processed waste residue (left unattended or discarded due to lack of appropriate skill and technology) can be taken into consideration and exploited for value-added materials.
    Matched MeSH terms: Mining
  2. Fulltext A review of subsequence time series clustering
    Zolhavarieh S, Aghabozorgi S, Teh YW
    ScientificWorldJournal, 2014;2014:312521.
    PMID: 25140332 DOI: 10.1155/2014/312521
    Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.
    Matched MeSH terms: Data Mining*
  3. Fulltext A new split based searching for exact pattern matching for natural texts
    Hakak S, Kamsin A, Shivakumara P, Idna Idris MY, Gilkar GA
    PLoS One, 2018;13(7):e0200912.
    PMID: 30048486 DOI: 10.1371/journal.pone.0200912
    Exact pattern matching algorithms are popular and used widely in several applications, such as molecular biology, text processing, image processing, web search engines, network intrusion detection systems and operating systems. The focus of these algorithms is to achieve time efficiency according to applications but not memory consumption. In this work, we propose a novel idea to achieve both time efficiency and memory consumption by splitting query string for searching in Corpus. For a given text, the proposed algorithm split the query pattern into two equal halves and considers the second (right) half as a query string for searching in Corpus. Once the match is found with second halves, the proposed algorithm applies brute force procedure to find remaining match by referring the location of right half. Experimental results on different S1 Dataset, namely Arabic, English, Chinese, Italian and French text databases show that the proposed algorithm outperforms the existing S1 Algorithm in terms of time efficiency and memory consumption as the length of the query pattern increases.
    Matched MeSH terms: Data Mining/methods*
  4. Fulltext Toward Financially Viable Phytoextraction and Production of Plant-Based Palladium Catalysts
    Harumain ZA, Parker HL, Muñoz García A, Austin MJ, McElroy CR, Hunt AJ, et al.
    Environ Sci Technol, 2017 03 07;51(5):2992-3000.
    PMID: 28191957 DOI: 10.1021/acs.est.6b04821
    Although a promising technique, phytoextraction has yet to see significant commercialization. Major limitations include metal uptake rates and subsequent processing costs. However, it has been shown that liquid-culture-grown Arabidopsis can take up and store palladium as nanoparticles. The processed plant biomass has catalytic activity comparable to that of commercially available catalysts, creating a product of higher value than extracted bulk metal. We demonstrate that the minimum level of palladium in Arabidopsis dried tissues for catalytic activity comparable to commercially available 3% palladium-on-carbon catalysts was achieved from dried plant biomass containing between 12 and 18 g·kg-1 Pd. To advance this technology, species suitable for in-the-field application: mustard, miscanthus, and 16 willow species and cultivars, were tested. These species were able to grow, and take up, palladium from both synthetic and mine-sourced tailings. Although levels of palladium accumulation in field-suitable species are below that required for commercially available 3% palladium-on-carbon catalysts, this study both sets the target, and is a step toward, the development of field-suitable species that concentrate catalytically active levels of palladium. Life cycle assessment on the phytomining approaches described here indicates that the use of plants to accumulate palladium for industrial applications has the potential to decrease the overall environmental impacts associated with extracting palladium using present-day mining processes.
    Matched MeSH terms: Mining*
  5. Fulltext A novel approach for heart disease prediction using strength scores with significant predictors
    Yazdani A, Varathan KD, Chiam YK, Malik AW, Wan Ahmad WA
    BMC Med Inform Decis Mak, 2021 06 21;21(1):194.
    PMID: 34154576 DOI: 10.1186/s12911-021-01527-5
    BACKGROUND: Cardiovascular disease is the leading cause of death in many countries. Physicians often diagnose cardiovascular disease based on current clinical tests and previous experience of diagnosing patients with similar symptoms. Patients who suffer from heart disease require quick diagnosis, early treatment and constant observations. To address their needs, many data mining approaches have been used in the past in diagnosing and predicting heart diseases. Previous research was also focused on identifying the significant contributing features to heart disease prediction, however, less importance was given to identifying the strength of these features.

    METHOD: This paper is motivated by the gap in the literature, thus proposes an algorithm that measures the strength of the significant features that contribute to heart disease prediction. The study is aimed at predicting heart disease based on the scores of significant features using Weighted Associative Rule Mining.

    RESULTS: A set of important feature scores and rules were identified in diagnosing heart disease and cardiologists were consulted to confirm the validity of these rules. The experiments performed on the UCI open dataset, widely used for heart disease research yielded the highest confidence score of 98% in predicting heart disease.

    CONCLUSION: This study managed to provide a significant contribution in computing the strength scores with significant predictors in heart disease prediction. From the evaluation results, we obtained important rules and achieved highest confidence score by utilizing the computed strength scores of significant predictors on Weighted Associative Rule Mining in predicting heart disease.

    Matched MeSH terms: Data Mining
  6. Science, politics, and radiation
    Wagner HN
    JAMA, 1988 Aug 5;260(5):697-8.
    PMID: 3392799
    Matched MeSH terms: Mining*
  7. Fulltext A novel association rule mining approach using TID intermediate itemset
    Aqra I, Herawan T, Abdul Ghani N, Akhunzada A, Ali A, Bin Razali R, et al.
    PLoS One, 2018;13(1):e0179703.
    PMID: 29351287 DOI: 10.1371/journal.pone.0179703
    Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.
    Matched MeSH terms: Data Mining/methods*
  8. 238U and 232Th in cassiterites samples and amang by-products
    Hu SJ, Chong CS, Subas S
    Health Phys, 1981 Feb;40(2):248-50.
    PMID: 7216807
    Matched MeSH terms: Mining*
  9. Gaining Insights on Nasopharyngeal Carcinoma Treatment Outcome Using Clinical Data Mining Techniques
    Ghaibeh AA, Kasem A, Ng XJ, Nair HLK, Hirose J, Thiruchelvam V
    Stud Health Technol Inform, 2018;247:386-390.
    PMID: 29677988
    The analysis of Electronic Health Records (EHRs) is attracting a lot of research attention in the medical informatics domain. Hospitals and medical institutes started to use data mining techniques to gain new insights from the massive amounts of data that can be made available through EHRs. Researchers in the medical field have often used descriptive statistics and classical statistical methods to prove assumed medical hypotheses. However, discovering new insights from large amounts of data solely based on experts' observations is difficult. Using data mining techniques and visualizations, practitioners can find hidden knowledge, identify interesting patterns, or formulate new hypotheses to be further investigated. This paper describes a work in progress on using data mining methods to analyze clinical data of Nasopharyngeal Carcinoma (NPC) cancer patients. NPC is the fifth most common cancer among Malaysians, and the data analyzed in this study was collected from three states in Malaysia (Kuala Lumpur, Sabah and Sarawak), and is considered to be the largest up-to-date dataset of its kind. This research is addressing the issue of cancer recurrence after the completion of radiotherapy and chemotherapy treatment. We describe the procedure, problems, and insights gained during the process.
    Matched MeSH terms: Data Mining*
  10. Fulltext Prediction of remaining useful life (RUL) of Komatsu excavator under reliability analysis in the Weibull-frailty model
    Ghomghaleh A, Khaloukakaie R, Ataei M, Barabadi A, Nouri Qarahasanlou A, Rahmani O, et al.
    PLoS One, 2020;15(7):e0236128.
    PMID: 32667940 DOI: 10.1371/journal.pone.0236128
    It is an essential task to estimate the remaining useful life (RUL) of machinery in the mining sector aimed at ensuring the production and the customer's satisfaction. In this study, a conceptual framework was used to determine the RUL under the reliability analysis in a frailty model. The proposed framework was implemented on a Komatsu PC-1250 excavator from the Sungun copper mine. Also, the Weibull-frailty model was selected to describe the failure behavior and compare it with the classical-exponential model. The frailty model was also used to evaluate the impact of unobserved environmental conditions on the RUL values. Both applied models were fitted to the obtained data from 80 operational hours of the Komatsu PC-1250 excavator. Plotting the results from the reliability analysis of two models demonstrated that the mine system with the frailty model performs better than the classical model before reaching the reliability of 80%. Besides, the frailty model shows a coherent with the operational time of the excavator, while the classical model demonstrates a sinusoid variation. The obtained results may be used for the development of maintenance, preventive repairs planning, and the spare parts replacement intervals.
    Matched MeSH terms: Mining/instrumentation*
  11. Fulltext Bioactive Molecule Prediction Using Extreme Gradient Boosting
    Babajide Mustapha I, Saeed F
    Molecules, 2016 Jul 28;21(8).
    PMID: 27483216 DOI: 10.3390/molecules21080983
    Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naïve Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets.
    Matched MeSH terms: Data Mining/methods*
  12. Passive bioremediation technology incorporating lignocellulosic spent mushroom compost and limestone for metal- and sulfate-rich acid mine drainage
    Muhammad SN, Kusin FM, Md Zahar MS, Mohamat Yusuff F, Halimoon N
    Environ Technol, 2017 Aug;38(16):2003-2012.
    PMID: 27745113 DOI: 10.1080/09593330.2016.1244568
    Passive bioremediation of metal- and sulfate-containing acid mine drainage (AMD) has been investigated in a batch study. Multiple substrates were used in the AMD remediation using spent mushroom compost (SMC), limestone, activated sludge (AS), and woodchips (WC) under anoxic conditions suitable for bacterial sulfate reduction (BSR). Limestones used were of crushed limestone (CLS) and uncrushed limestone, provided at two different ratios in mixed substrates treatment and varied by the proportion of SMC and limestone. The SMC greatly assisted the removals of sulfate and metals and also acted as an essential carbon source for BSR. The mixed substrate composed of 40% CLS, 30% SMC, 20% AS, and 10% WC was found to be effective for metal removal. Mn, Cu, Pb, and Zn were greatly removed (89-100%) in the mixed substrates treatment, while Fe was only removed at 65%. Mn was found to be removed at a greatly higher rate than Fe, suggesting important Mn adsorption onto organic materials, that is, greater sorption affinity to the SMC. Complementary with multiple treatment media was the main mechanism assisting the AMD treatment through microbial metal reduction reactions.
    Matched MeSH terms: Mining*
  13. Pollutant source, ecological and human health risks assessment of heavy metals in soils from coal mining areas in Xinjiang, China
    Zhang H, Zhang F, Song J, Tan ML, Kung HT, Johnson VC
    Environ Res, 2021 11;202:111702.
    PMID: 34284019 DOI: 10.1016/j.envres.2021.111702
    This study aims to analyze the pollution characteristics and sources of heavy metal elements for the first time in the Zhundong mining area in Xinjiang using the linear regression model. Additionaly, the health risks with their probability and infleuencing factors on different groups of people's were also evaluated using Monte Carlo (MC) simulation approach. The results shows that 89.28% of Hg was from coal combustion, 40.28% of Pb was from transportation, and 19.54% of As was from atmospheric dust. The main source of Cu and Cr was coal dust, Hg has the greatest impact on potential ecological risks. which accounted for 60.2% and 81.46% of the Cu and Cr content in soil, respectively. The all samples taken from Pb have been Extremely polluted (100%). 93.3% samples taken from As have been Extremely polluted. The overall potential ecological risk was moderate. Adults experienced higher non-carcinogenic risks of heavy metals from their diets than children. Interestingly, body weight was the main factor affecting the adult's health risks. This research provides more comprehensive information for better soil management, soil remediation, and soil pollution control in the Xinjiang mining areas.
    Matched MeSH terms: Coal Mining*
  14. Mine land reclamation, mine land reuse, and vegetation cover change: An intriguing case study in Dartford, the United Kingdom
    Yu H, Zahidi I, Liang D
    Environ Res, 2023 May 15;225:115613.
    PMID: 36870554 DOI: 10.1016/j.envres.2023.115613
    Dartford, a town in England, heavily relied on industrial production, particularly mining, which caused significant environmental pollution and geological damage. However, in recent years, several companies have collaborated under the guidance of the local authorities to reclaim the abandoned mine land in Dartford and develop it into homes, known as the Ebbsfleet Garden City project. This project is highly innovative as it not only focuses on environmental management but also provides potential economic benefits, employment opportunities, builds a sustainable and interconnected community, fosters urban development and brings people closer together. This paper presents a fascinating case that employs satellite imagery, statistical data, and Fractional Vegetation Cover (FVC) calculations to analyse the re-vegetation progress of Dartford and the development of the Ebbsfleet Garden City project. The findings indicate that Dartford has successfully reclaimed and re-vegetated the mine land, maintaining a high vegetation cover level while the Ebbsfleet Garden City project has advanced. This suggests that Dartford is committed to environmental management and sustainable development while pursuing construction projects.
    Matched MeSH terms: Mining*
  15. 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
  16. Norway's mining plans threaten Arctic life
    Sonne C, Ciesielski TM, Jenssen BM, Lam SS, Zhong H, Dietz R
    Science, 2023 Aug 25;381(6660):843-844.
    PMID: 37616344 DOI: 10.1126/science.adj4244
    Matched MeSH terms: Mining*
  17. Fulltext Adapting Document Similarity Measures for Ligand-Based Virtual Screening
    Himmat M, Salim N, Al-Dabbagh MM, Saeed F, Ahmed A
    Molecules, 2016 Apr 13;21(4):476.
    PMID: 27089312 DOI: 10.3390/molecules21040476
    Quantifying the similarity of molecules is considered one of the major tasks in virtual screening. There are many similarity measures that have been proposed for this purpose, some of which have been derived from document and text retrieving areas as most often these similarity methods give good results in document retrieval and can achieve good results in virtual screening. In this work, we propose a similarity measure for ligand-based virtual screening, which has been derived from a text processing similarity measure. It has been adopted to be suitable for virtual screening; we called this proposed measure the Adapted Similarity Measure of Text Processing (ASMTP). For evaluating and testing the proposed ASMTP we conducted several experiments on two different benchmark datasets: the Maximum Unbiased Validation (MUV) and the MDL Drug Data Report (MDDR). The experiments have been conducted by choosing 10 reference structures from each class randomly as queries and evaluate them in the recall of cut-offs at 1% and 5%. The overall obtained results are compared with some similarity methods including the Tanimoto coefficient, which are considered to be the conventional and standard similarity coefficients for fingerprint-based similarity calculations. The achieved results show that the performance of ligand-based virtual screening is better and outperforms the Tanimoto coefficients and other methods.
    Matched MeSH terms: Data Mining*
  18. Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus
    Idris NF, Ismail MA, Jaya MIM, Ibrahim AO, Abulfaraj AW, Binzagr F
    PLoS One, 2024;19(5):e0302595.
    PMID: 38718024 DOI: 10.1371/journal.pone.0302595
    Diabetes Mellitus is one of the oldest diseases known to humankind, dating back to ancient Egypt. The disease is a chronic metabolic disorder that heavily burdens healthcare providers worldwide due to the steady increment of patients yearly. Worryingly, diabetes affects not only the aging population but also children. It is prevalent to control this problem, as diabetes can lead to many health complications. As evolution happens, humankind starts integrating computer technology with the healthcare system. The utilization of artificial intelligence assists healthcare to be more efficient in diagnosing diabetes patients, better healthcare delivery, and more patient eccentric. Among the advanced data mining techniques in artificial intelligence, stacking is among the most prominent methods applied in the diabetes domain. Hence, this study opts to investigate the potential of stacking ensembles. The aim of this study is to reduce the high complexity inherent in stacking, as this problem contributes to longer training time and reduces the outliers in the diabetes data to improve the classification performance. In addressing this concern, a novel machine learning method called the Stacking Recursive Feature Elimination-Isolation Forest was introduced for diabetes prediction. The application of stacking with Recursive Feature Elimination is to design an efficient model for diabetes diagnosis while using fewer features as resources. This method also incorporates the utilization of Isolation Forest as an outlier removal method. The study uses accuracy, precision, recall, F1 measure, training time, and standard deviation metrics to identify the classification performances. The proposed method acquired an accuracy of 79.077% for PIMA Indians Diabetes and 97.446% for the Diabetes Prediction dataset, outperforming many existing methods and demonstrating effectiveness in the diabetes domain.
    Matched MeSH terms: Data Mining/methods
  19. Remote sensing-based seasonal surface urban heat island analysis in the mining and industrial environment
    Halder B, Bandyopadhyay J, Ghosh N
    Environ Sci Pollut Res Int, 2024 May;31(25):37075-37108.
    PMID: 38760605 DOI: 10.1007/s11356-024-33603-4
    Cooling spaces have an optimistic influence on surface urban heat islands (SUHI). Blue spaces benefit from balancing the changing climate and heat variations. Because of the rapid deforestation and SUHI increase, the climate is gradually changing in Paschim Bardhhaman, West Bengal state, India. Paschim Bardhhaman has two sectors: specifically, Durgapur is the main industrial centre and Asansol has coal mines. This investigation aims to categorize spatiotemporal variations and seasonal differences in cooling spaces and their influence on SUHI, land use and land cover (LULC), and thermal differences using Landsat datasets for the years 1992, 2004, 2012, and 2022 in summer and winter. The coal mining and industrial range decreased from 10,391.92 (1992) to 3591.1 ha (2022), respectively. Open pit mining distresses fresh water by heavy water uses in ore processing, and mining water was applied to excerpt minerals. Among the two sub-divisions, the blue space amount was higher in Asansol because mining actions were higher in Asansol than in Durgapur. The open vegetation volume has reduced from 46,441.03 (1992) to 25,827.55 ha (2022) and dense vegetation has erased from 7368.02 (1992) to 15,608.56 ha (2022). Dense vegetation improved because of heavy precipitation in those regions. Mostly, Raghunathpur, Saraswatiganja, Bhagabanpur, Bistupur, Paschim Gangaram, Garkilla Kherobari, and Gourbazar have dense vegetation. The outcomes similarly demonstrate that the total built-up part has increased by 8412.82 ha in between 30 years. The built-up zone changes near the southeast and western Paschim Bardhhaman district. Those region needs appropriate attention and planning to survive soon.
    Matched MeSH terms: Mining*
  20. 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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