Evaluation of Image Filtering Parameters for Plant Biometrics Improvement Using Machine Learning

Olaleye, Taiwo; Arogundade, Oluwasefunmi ‘Tale; Adenusi, Cecelia; Misra, Sanjay; Bello, A.

doi:10.1007/978-981-16-0708-0_25

Cited by 6 publications

(2 citation statements)

References 28 publications

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“…Machine learning is a branch of artificial intelligence that trains algorithms with data, in either supervised or unsupervised learning approach, and thereby gives capabilities to learn from data without being explicitly programmed and in turn make informed decisions after the learning process in what is referred to as testing the knowledge gain [22]. REPTree is reputable as a quick decision tree learner-algorithm which constructs a decision or regression tree with information gain as the splitting methodology, and prunes it with reduced error pruning method as it results in a more accurate classification tree; size of training and testing notwithstanding.…”

Section: Reptree Predictive Modellingmentioning

confidence: 99%

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Olaleye¹

2022

IJRASET

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Data has indisputably proven overtime to have a better idea and with the surge of big data in the era of coronavirus, research initiatives in the field of data mining continues to leverage computational methodologies. Owing to the dreadful nature of the Omicron-variant, a fight or flight dilemma readily pervades college communities far reaching implications on work ethics of academic front-liners. This study therefore aim to gain insights from academia-sourced data to unravel fear-stimulus in college communities. The predictive analytics is carried out on college-based opinion poll. The Valence Aware Dictionary for Sentiment Reasoning algorithm is deployed for emotion analytics while the Latent Dirichlet Allocation (LDA) and Latent Semantic Indexing (LSI) are employed for topic modelling. The REPTree algorithm models the fear-spotting decision tree using 10-fold cross-validation. Experimental results shows a high performance metrics of 94.68% on Recall and Precision as the hand-washing attribute is returned as the most significant variable with highest information gain. Results of topic modelling likewise returns non-clinical precautionary measures as fear stimulus while the Vader sentiment analysis shows a 22.47%, 25.8%, and 51.73% positive, negative, and neutral polarity scores respectively, indicative of the academic front-liners’ pessimism towards effective safety measure compliance with non-clinical regulations. Keywords: COVID-19, Omicron, Sentiment Analysis, Topic Modelling.

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Section: Reptree Predictive Modellingmentioning

confidence: 99%

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Olaleye¹

2022

IJRASET

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“…As technology and analytical methods have rapidly evolved since that time, there exists an unprecedented opportunity to reevaluate this historic dataset. New data science techniques, including Data Visualization, Correlation Analysis, and Differential Expression Analysis, stand poised to illuminate latent knowledge within historical cancer datasets, thereby offering vital contributions to contemporary cancer research and understanding (Taiwo Olaleye, 2021). Data Visualization, driven by advanced visualization tools, enables the exploration of complex gene expression profiles in an intuitive manner.…”

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confidence: 99%

Improving Acute Leukemia Classification through Recursive Feature Elimination and Multilayer Perceptron Analysis of Gene Expression Data

Ogunbiyi,

Adegoke,

Oluwatosin

et al. 2024

Int. J. Data. Science.

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This study presents an approach to improving the classification of acute leukemia subtypes using gene expression data analysis. Leveraging Recursive Feature Elimination (RFE) as a feature selection technique and Multilayer Perceptron (MLP) as the predictive modeling framework, this research aims to identify the most influential genes for distinguishing between Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) cases. RFE systematically ranks and selects the most discriminative gene attributes, while MLP constructs a predictive model based on these attributes. The results demonstrate the effectiveness of this combined approach, achieving precision, accuracy, F1-Score, and recall rates of approximately 99% for leukemia subtype classification. Furthermore, specific genes contributing most to the model's predictive power and shedding light on potential biomarkers for leukemia diagnosis were identified. This research underscores the significance of RFE and MLP in the analysis of gene expression data and their potential impact on clinical decision-making in the field of oncology.

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A Systematic Literature Review on Face Morphing Attack Detection (MAD)

Kenneth

Bashir

Abdulhamid

et al. 2022

Illumination of Artificial Intelligence in Cybersecurity and Forensics

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Evaluation of Image Filtering Parameters for Plant Biometrics Improvement Using Machine Learning

Cited by 6 publications

References 28 publications

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Opinion Mining Analytics for Spotting Omicron Fear-Stimuli Using REPTree Classifier and Natural Language Processing

Improving Acute Leukemia Classification through Recursive Feature Elimination and Multilayer Perceptron Analysis of Gene Expression Data

A Systematic Literature Review on Face Morphing Attack Detection (MAD)

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