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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
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Watershed Planning within a Quantitative Scenario Analysis Framework
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Predictive analytics technique based on hybrid sampling to manage unbalanced data in smart cities.

Ayushi Chahal1, Preeti Gulia1, Nasib Singh Gill1

  • 1Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak, Haryana, India.

Heliyon
|January 6, 2025
PubMed
Summary

This study introduces a new predictive analytics method to handle imbalanced data in smart city IoT systems. The technique improves decision-making by balancing data before machine learning prediction.

Keywords:
ENNInternet of Things (IoT)Machine learningPCAPredictive analyticsSMOTESensors

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Area of Science:

  • Smart City Technologies
  • Data Science
  • Artificial Intelligence

Background:

  • Smart cities rely on Artificial Intelligence (AI) for autonomous decision-making, requiring extensive data from the physical world.
  • Internet of Things (IoT) sensor devices collect environmental data for predictive analytics, but this data is often imbalanced.
  • Imbalanced data, if not pre-processed, can lead to significant errors in AI-driven decisions.

Purpose of the Study:

  • To propose a novel predictive analytical technique for managing imbalanced data in smart city IoT environments.
  • To develop a robust pipeline integrating data pre-processing and machine learning for accurate predictions.
  • To enhance the decision-making capabilities of smart cities by addressing data imbalance challenges.

Main Methods:

  • A pipeline was designed incorporating Principal Component Analysis (PCA), a hybrid sampling method (SMOTE+ENN), and a Machine Learning (ML) prediction model.
  • SMOTE+ENN was employed to transform imbalanced datasets into a balanced state.
  • ML algorithms were utilized for clustering and making predictions on the processed dataset, using a large Smart City IoT dataset (405,184 records).

Main Results:

  • The proposed technique accurately predicts human presence near IoT devices.
  • Evaluation metrics including accuracy, precision, recall, F1-score, and Area Under Curve (AUC) demonstrated strong performance.
  • For cluster 0, accuracy was 0.79, precision 1.0, recall 0.79, F1-score 0.87, and AUC 0.88. For cluster 1, accuracy was 0.86, precision 0.99, recall 0.86, F1-score 0.92, and AUC 0.92.

Conclusions:

  • The developed technique effectively manages imbalanced data for predictive analytics in smart cities.
  • The proposed method shows promise for improving decision-making in various real-world applications.
  • The integration of PCA, SMOTE+ENN, and ML offers a robust solution for smart city IoT data challenges.