Detection of Drug Abuse Using Rough Set and Neural Network-Based Elevated Mathematical Predictive Modelling

Selvi, S.; Chandrasekaran, M.

doi:10.1007/s11063-022-11086-z

Cited by 2 publications

(1 citation statement)

References 46 publications

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“…The proposed hybrid approach aims to leverage the advantages of grid partitioning, rough set theory, and fuzzy logic to enhance dataset classification (Selvi & Chandrasekaran, 2022) (Behera et al, 2023). By combining grid partitioning with rough set theory, the approach seeks to identify the most relevant attributes within each grid cell, effectively reducing the dimensionality and focusing on local patterns (Steinbach et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Optimizing dataset classification through hybrid grid partition and rough set method for fuzzy rule generation

Velo,

Tamatave,

Sahambala

2023

emod

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This research presents a novel approach for optimizing dataset classification through the integration of a hybrid grid partition and rough set method for fuzzy rule generation. The objective is to improve classification accuracy and interpretability while effectively handling uncertainty in the dataset. The proposed approach combines grid partitioning, rough set theory, and fuzzy logic to identify relevant attributes within each grid cell, generate accurate fuzzy rules, and perform classification based on fuzzy inference. The research demonstrates the improved accuracy of the hybrid approach compared to traditional methods, along with enhanced interpretability of the generated fuzzy rules. The scalability and generalizability of the approach are validated through its application to a case example in customer churn prediction in the telecommunications industry. However, certain limitations, such as the selection of the partitioning scheme, computational complexity, and handling of missing data, need to be considered. Further research is required to address these limitations and benchmark the approach against state-of-the-art techniques. The proposed hybrid approach contributes to the field of dataset classification by offering an effective and interpretable methodology for improved classification performance and actionable insights in real-world applications

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Section: Introductionmentioning

confidence: 99%

Optimizing dataset classification through hybrid grid partition and rough set method for fuzzy rule generation

Velo,

Tamatave,

Sahambala

2023

emod

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Analyzing Campus Drug Abuse Dynamics Through an Infectious Disease Framework

Gogovi,

Qin

2024

Journal of Applied Mathematics

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This paper introduces a mathematical model to elucidate the transmission dynamics of drug abuse within a college environment, focusing on rehabilitation measures and recovery. By calculating the basic reproduction number (R0) and integrating it with significant parameters for drug‐free and drug‐persistent equilibria, we reveal the nuanced impact of rehabilitation on the spread of drug abuse. Our findings indicate that the drug‐free equilibrium of the model is locally asymptotically stable when R0 < 1 and the average number of susceptible individuals who become aware of the use of substances, generated by exposed individuals, is less than one. In contrast, the equilibrium becomes unstable when R0 > 1. Numerical simulations demonstrate that increasing rehabilitation and recovery rates can reduce R0 below unity. However, the effectiveness of these measures may be influenced by factors such as the availability and type of rehabilitation facilities, the quality of rehabilitation programs, and social support systems for recovering students. Further analysis using contour plots highlights that effective control of drug abuse can be achieved with high rehabilitation and recovery rates. The sensitivity analysis of the parameters further underscores the importance of targeting interventions that reduce the transmission rate (κ), such as preventive programs, awareness campaigns, or restrictions on access to drugs. In addition, the study proposes a combination of control strategies, including continuously recruiting a large portion of the infected student population into rehabilitation programs. Collectively, these measures offer promising approaches to managing and potentially eradicating drug abuse within college settings.

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Detection of Drug Abuse Using Rough Set and Neural Network-Based Elevated Mathematical Predictive Modelling

Cited by 2 publications

References 46 publications

Optimizing dataset classification through hybrid grid partition and rough set method for fuzzy rule generation

Optimizing dataset classification through hybrid grid partition and rough set method for fuzzy rule generation

Analyzing Campus Drug Abuse Dynamics Through an Infectious Disease Framework

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