Diagnosis of Long Sightedness Using Neural Network and Decision Tree Algorithms

Ogundokun, Roseline Oluwaseun; Sadiku, Peter O.; Misra, Sanjay; Ogundokun, Opeyemi Eyitayo; Awotunde, Joseph Bamidele; Jaglan, Vivek

doi:10.1088/1742-6596/1767/1/012021

Cited by 11 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DT is a supervised natural learning process that resembles a tree with roots and leaves. DT has many benefits over other machine learning methods, including ease of implementation, ability to handle massive datasets, and transparency [35,36]. In defense applications such as DDoS and intrusion detection, DTs are commonly used as classifiers [37].…”

Section: Decision Tree (Dt)mentioning

confidence: 99%

An Optimization Feature Extraction Technique using Intrusion Detection System

2023

IRJMETS

View full text Add to dashboard Cite

As the alarming growth of computer connectivity and the significant number of computer-related applications increased lately, the challenge of achieving cyber-security has become progressively difficult. It also needs an adequate defense mechanism against a variety of cyber attacks. Detecting inconsistencies and threats in a computer network, as well as designing intrusion detection systems (IDS) that can help with cyber-security. A useful data-driven intrusion detection system has been developed using artificial intelligence (AI), particularly machine learning (ML) techniques. In this study, two different classification techniques for intrusion detection (ID) with each having its unique use cases were compared. Particle Swarm Optimization (PSO) algorithm was employed for dimensionality reduction before employing the two classifiers for the classification procedure. This study considered the classification techniques to categorize the network anomalies. The two classifiers employed are PSO + Decision Tree (PSO+DT) and PSO + K-Nearest Neighbor (PSO+KNN). The results of the detection techniques were verified using KDD-CUP 99 dataset. The usefulness of success indicators such as specificity, recall, f1-score, accuracy, precision, and consistency on cyber-security databases for different categories of cyber-attacks was employed on the result of the implementation. The two classifiers were also compared to deduce which of the classifier achieves exceedingly in terms of accuracy, detection rate (DR), and false-positive rate (FPR). Finally, the system was compared with the existing IDS. The results show that PSO+KNN outperformed the PSO+DT classifier algorithm in terms of identifying network anomalies.

show abstract

Section: Decision Tree (Dt)mentioning

confidence: 99%

An Optimization Feature Extraction Technique using Intrusion Detection System

2023

IRJMETS

View full text Add to dashboard Cite

show abstract

“…Large amounts of data play a crucial role in the development of technologies for breast cancer screening. Data mining is thought to be the discipline in charge of evaluating large amounts of information [21].…”

Section: Introductionmentioning

confidence: 99%

Comparing the Performance of Various Supervised Machine Learning Techniques for Early Detection of Breast Cancer

Abiodun

Misra

Awotunde

et al. 2022

Lecture Notes in Networks and Systems

Self Cite

View full text Add to dashboard Cite

Cancer is a fatal disease that is constantly changing and affects a vast number of individuals worldwide. At the research level, much work has gone into the creation and improvement of techniques built on data mining approaches that allow for the early identification and prevention of breast cancer. Because of its excellent diagnostic abilities and effective classification, data mining technologies have a reputation in the medical profession that is continually increasing. Data mining and machine learning approaches can aid practitioners in conceiving and developing tools to aid in the early detection of breast cancer. As a result, the goal of this research is to compare different machine learning algorithms in order to determine the best way for detecting breast cancer promptly. This study assessed the classification accuracy of four machine learning algorithms: KNN, Decision Tree, Naive Bayes, and SVM in order to find the best accurate supervised machine learning algorithm that might be used to diagnose breast cancer. Naive Bayes has the maximum accuracy for the supplied dataset, according to the prediction results. This reveals that, when compared to KNN, SVM, and Decision Tree, Naive Bayes can be utilized to predict breast cancer.

show abstract