Diagnostic analysis for outlier detection in big data analytics

Ridzuan, Fakhitah; Zainon, Wan Mohd Nazmee Wan

doi:10.1016/j.procs.2021.12.189

Cited by 8 publications

(5 citation statements)

References 17 publications

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“…In Other domains, the Global Food Price dataset, assessing the level of sustainability of food choices available in the market to achieve zero hunger, was diagnosed for outlier detection [173]. In climate research, "contemporary earth observation sensors such as multispectral, hyperspectral, microwave, and LiDAR provide high spatial and spectral resolution data utilizing diverse wavelength regions of the electromagnetic spectrum" [144] (pp.…”

Section: Toolsmentioning

confidence: 99%

“…For instance, in the Governance domain, a novel strategic culture indicator was proposed [173] as a more accurate governance indicator, measuring the variety of urban strategic planning processes taking place in a territory over time. The 13 Sustainable Governance Indicators (SGI) by [73] were used to measure different aspects of governance from the SDG perspective.…”

Section: Indicatorsmentioning

confidence: 99%

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Progress by Research to Achieve the Sustainable Development Goals in the EU: A Systematic Literature Review

et al. 2023

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Scientific research has been acknowledged to play a pivotal role in achieving the United Nations’ 2030 Agenda. Vice-versa, since its adoption, the 2030 Agenda has been reinvigorating the academic production on sustainable development. This study provides a systematic literature review of the most used and newly developed approaches by academic research to support the achievement of the SDGs in the EU. The results are presented by descriptive, bibliometric, and content analysis. The descriptive analysis highlights a rising interest of scholars in operationalizing the 2030 Agenda, with a growing interest at the urban level. A text-mining tool was employed to scan the most investigated SDGs in the selected papers. Major interest by scholars is devoted to environmental concerns (especially linked to SDG 13, 7, 6, 12, and 15), while social issues (e.g., SDG 4, 5, and 10) still deserve more research. The bibliometric analysis unveiled poor intra-cluster connections, highlighting the need for more transdisciplinary research. The most recurrent research fields on the SDGs in the EU are governance, circular economy, ecosystem services, urban localization, and decision making. We advise future studies to focus on gaps highlighted and adopt a system perspective, boosting Policy Coherence across governance levels and scales of implementation by looking at trade-offs and assessing context-specific priorities.

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Section: Toolsmentioning

confidence: 99%

Section: Indicatorsmentioning

confidence: 99%

Progress by Research to Achieve the Sustainable Development Goals in the EU: A Systematic Literature Review

et al. 2023

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“…However, data quality is one of the most critical data management issues as data from various sources and formats become available to ensure the usability of the data 21 . A characteristic shared by these new emerging types of data sources is represented by the uncontrolled presence of both sampling and non-sampling errors.…”

Section: Background and Summarymentioning

confidence: 99%

From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling

et al. 2023

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Timely and reliable monitoring of food market prices at high spatial and temporal resolution is essential to understanding market and food security developments and supporting timely policy and decision-making. Mostly, decisions rely on price expectations, which are updated with new information releases. Therefore, increasing the availability and timeliness of price information has become a national and international priority. We present two new datasets in which mobile app-based crowdsourced daily price observations, voluntarily submitted by self-selected participants, are validated in real-time within spatio-temporal markets (pre-processed data). Then, they are reweighted weekly using their geo-location to resemble a formal sample design and allow for more reliable statistical inference (post-sampled data). Using real-time data collected in Nigeria, we assess the accuracy and propose that our reweighted estimates are more accurate with respect to the unweighted version. Results have important implications for governments, food chain actors, researchers and other organisations.

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“…-The CUA is one of the popular ranking-type measures. In [28]the CUA was used to build an optimized learning model and to compare learning algorithms [29]. Unlike the threshold and probability metrics, the AUC value reflects the overall ranking performance of a classifier.…”

Section: The Model's Measurementsmentioning

confidence: 99%

Predictive Analysis of Diabetes Without Data Pre-Processing via the Evaluation of Tree Algorithms

Kolo¹,

Edwige²,

Hyacinthe³

et al. 2022

IJAR

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Diabetes is a common disease, incurable and fatal in its complication phases. Its management, like many other metabolic diseases, remains a scientific challenge. Mathematical approaches have been used to understand this scourge and artificial intelligence is used to model its prediction. In general, the effectiveness and efficiency of an artificial intelligence solution depends on the nature and characteristics of the data and the performance of the learning methods. Hence the interest in the quality of the data and the performance of the methods used to model such a task. In order to find a suitable artificial intelligence model for diabetes prediction, several studies have used methods from different techniques. Thus, diabetes prediction has been addressed using machine learning methods, neural networks, deep learning, Bayesian naive classification, K-nearest neighbors and machine vector support. In order to compare the performance to determine the best model, several of these methods are analyzed in previous studies. Thus, this paper evaluates the methods based on the decision tree technique (DT, RF, LightGBM, Adaboost and XGBoost), based on the PIMA Diabetes Indian data (PID). The aim is to show the predictive ability of the methods of this technique and to determine the appropriate method for predicting diabetes with raw data. The PIMA data are described statistically, and the comparative analysis of the models is performed following K-fold cross-validation, before and after class balancing. At the end of the experiment, the best results are obtained by LightGBM, XGBoostand RF on different metrics.

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Diagnostic analysis for outlier detection in big data analytics

Cited by 8 publications

References 17 publications

Progress by Research to Achieve the Sustainable Development Goals in the EU: A Systematic Literature Review

Progress by Research to Achieve the Sustainable Development Goals in the EU: A Systematic Literature Review

From mobile crowdsourcing to crowd-trusted food price in Nigeria: statistical pre-processing and post-sampling

Predictive Analysis of Diabetes Without Data Pre-Processing via the Evaluation of Tree Algorithms

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