An Efficient Framework for classifying Cancer diseases using Ensemble machine learning over Cancer Gene Expression and Sequence Based Protein Interactions.

Metipatil, Prabhuraj; Bhuvaneshwari, P.; Basha, Syed Muzamil; Patil, Siddanagouda S

doi:10.1109/inocon57975.2023.10101354

Cited by 3 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NB deals with features as they are independent of each other. This algorithm is fast and efficient with highdimensional datasets [18].…”

Section: And DL Modelsmentioning

confidence: 99%

Employing Hybrid ANOVA-RFE with Machine and Deep Learning Models for Enhanced IoT and IIoT Attack Detection and Classification

Yassen,

Abdulrazzq,

Mohammed

2023

ISI

View full text Add to dashboard Cite

The Internet of Things (IoT) has become an integral component in various applications, with significant prominence in healthcare and cybersecurity sectors. It is indispensable in medical diagnostics, monitoring, decision-support systems, and the safeguarding of sensitive data. However, the traditional methodologies have shown limitations in their ability to detect and classify all types of attacks effectively. This study presents a robust feature selection model, ANOVA-Recursive Feature Elimination (ANOVA-RFE), implemented with both Machine and Deep Learning paradigms, aiming to augment the security level by enhancing attack detection and classification. The models were trained using both the entire feature set and the selected features identified by ANOVA-RFE, demonstrating the efficiency and precision of the proposed method. The experiments yielded an accuracy of 100% and 99.96% using only the top five selected features from the first and second datasets, respectively. Furthermore, the performance of Gaussian Naive Bayes (GNB), K-Nearest Neighbors (K-NN), Random Forest (RF), AdaBoost (AB), Logistic Regression (LR), Decision Tree (DT), and Long Short-Term Memory (LSTM) models are evaluated, showcasing their respective accuracies on the first dataset. A scorelevel fusion was also employed, and the results were benchmarked against the current stateof-the-art, validating the robustness and high precision of the current study. Future work should consider analyzing different datasets and addressing further challenges.

show abstract

“…NB deals with features as they are independent of each other. This algorithm is fast and efficient with highdimensional datasets [18].…”

Section: And DL Modelsmentioning

confidence: 99%

Employing Hybrid ANOVA-RFE with Machine and Deep Learning Models for Enhanced IoT and IIoT Attack Detection and Classification

Yassen,

Abdulrazzq,

Mohammed

2023

ISI

View full text Add to dashboard Cite

show abstract

Efficient Melanoma Disease Detection by Using Convolutional Neural Network

Manukumaar,

Reddy,

Metipatil

2024

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

Taha

2023

Preprint

View full text Add to dashboard Cite

This survey paper provides an in-depth analysis of various machine learning techniques and algorithms that are utilized in the detection of PPI (Protein-Protein Interactions). For every technique examined, the paper evaluates its efficiency, shortcomings, possibilities for enhancement, and outlook for the future. A major challenge in current survey papers focusing on machine learning algorithms for PPI identification is the successful categorization of these algorithms. To overcome this challenge, the paper introduces a novel hierarchical taxonomy that organizes algorithms into more intricate categories and distinct techniques. The proposed taxonomy is constructed on a four-tier structure, beginning with the broad methodology category, and ending with specific sub-techniques. This structure facilitates a more systematic and exhaustive categorization of algorithms, aiding researchers in grasping the connections between different algorithms and techniques. Included in the paper are both empirical and experimental assessments to classify the various techniques. The empirical assessment judges the techniques according to four standards. The experimental evaluations carry out the following rankings: (1) the algorithms that employ the same specific sub-technique, (2) the different sub-techniques that employ the same technique, (3) the different techniques that employ the same methodology sub-category, and (4) the different methodology sub-categories within the same methodology category. By merging the new methodological taxonomy, empirical analyses, and experimental evaluations, the paper provides a multifaceted and thorough comprehension of the machine learning methods and algorithms for PPI detection. This synthesis helps researchers make well-informed decisions. In its conclusion, the paper furnishes crucial insights into the future possibilities of machine learning techniques for PPI identification, underscoring potential advancements and areas ripe for continued exploration and development

show abstract

An Efficient Framework for classifying Cancer diseases using Ensemble machine learning over Cancer Gene Expression and Sequence Based Protein Interactions.

Cited by 3 publications

References 25 publications

Employing Hybrid ANOVA-RFE with Machine and Deep Learning Models for Enhanced IoT and IIoT Attack Detection and Classification

Employing Hybrid ANOVA-RFE with Machine and Deep Learning Models for Enhanced IoT and IIoT Attack Detection and Classification

Efficient Melanoma Disease Detection by Using Convolutional Neural Network

Employing Machine Learning Techniques to Detect Protein-Protein Interaction: A Survey, Experimental, and Comparative Evaluations

Contact Info

Product

Resources

About