Factors determining customers desire to analyse supply chain management in intelligent IoT

Ferinia, Rolyana; Kumar, Dasari Lokesh Sai; Kumar, B. Santhosh; Muthu, Bala Anand; Asaad, Renas Rajab; Ramamoorthi, Jaya Subalakshmi; Daniel, Jérémie

doi:10.1007/s10878-023-01007-8

Cited by 6 publications

(1 citation statement)

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“…The principal objective of this work is to evaluate the Iris dataset using classification and regression techniques, with an emphasis on analyzing and examining patterns derived from the size of the flower's sepal and petal. The results of the study show that the Support Vector Machine (SVM) classifier outperforms the K-Nearest Neighbors (KNN) and Logistic Regression models in terms of accuracy, highlighting its utility in classification applications [42].…”

Section: B Literature Reviewmentioning

confidence: 99%

Comprehensive Classification of Iris Flower Species: A Machine Learning Approach

Renas Rajab Asaad,

M. Abdulazeez

2024

ijcs

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This study employs robust machine learning techniques to comprehensively assess the classification of Iris flower species. This study investigates the effectiveness of several machine learning algorithms in reliably classifying Iris flower species by utilizing a dataset that includes crucial morphological attributes such as sepal length, sepal width, petal length, and petal width. The algorithms under consideration are Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and XGBoost. Every algorithm has its own distinct methodology for classification, where Decision Trees offer clear interpretability and Random Forest and XGBoost offer strong and complex ensemble techniques. The primary aim of this study is to assess and contrast different algorithms, considering not only their classification accuracy but also significant performance metrics including precision, recall, F1-score, ROC AUC, and specificity. This research provides valuable insights into the capabilities and constraints of each methodology when implemented on a meticulously organized and defined botanical dataset. It is expected that the results of this study will contribute significantly to the fields of artificial intelligence and botanical taxonomy, highlighting the capacity of these methods to accurately identify and categorize plant species.

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Section: B Literature Reviewmentioning

confidence: 99%