Predicting Parcel Damage Through Pattern Extraction and Machine Learning

This research explores parcel-damage prediction using multiple pattern-extraction methods, including Apriori, FP-Growth, K-Means clustering, logistic regression, and a custom linear-algebra–based approach, for both pattern observation or direct prediction.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-26
DOI
https://doi.org/10.5281/zenodo.17717916
Primary Topic
Financial Distress and Bankruptcy Prediction
Type
article
Field-Weighted Citation Impact
0.00
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article

Predicting Parcel Damage Through Pattern Extraction and Machine Learning

Zenodo (CERN European Organization for Nuclear Research)
Financial Distress and Bankruptcy Prediction
article

Predicting Parcel Damage Through Pattern Extraction and Machine Learning

article en

Abstract

This research explores parcel-damage prediction using multiple pattern-extraction methods, including Apriori, FP-Growth, K-Means clustering, logistic regression, and a custom linear-algebra–based approach, for both pattern observation or direct prediction.

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 31%
Financial Distress and Bankruptcy Prediction
DOIOpen Access PDFOpenAlex
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Predicting Parcel Damage Through Pattern Extraction and Machine Learning · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS