Design of an Efficient Face Recognition system using Deep Learning Technique

The primary contributor to lung cancer is an abnormal proliferation of lung cells. Tobacco usage and smoking cigarettes are the primary contributors to the development of lung cancer. The most common forms of lung cancer fall into two distinct types. Non-small-cell lung cancers and small-cell lung cancers are the two primary subtypes of lung cancer. A computed tomography, or CT, scan is an essential diagnostic technique that may determine the kind of cancer a patient has, its stage, the location of any metastases, and the degree to which it has spread to other organs. Other diagnostic tools include biopsies and pathology tests. The creation of algorithms that allow computers to gain information and abilities by seeing and interacting with the world around them is the core emphasis of the field of machine learning. This article demonstrates how to detect lung cancer via the use of machine learning by using improved feature selection and image processing. Image quality may be improved with the help of the CLAHE algorithm. The K Means technique is used in order to segment a picture into its component components. In order to determine which traits are beneficial, the PSO algorithm is utilised. The photos are then categorised using the SVM, ANN, and KNN algorithms respectively. It uses images obtained from a CT scan. When it comes to detecting lung cancer, PSO SVM provides more accurate results.

show abstract

Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer Detection

Nancy

Kishan

Rane

et al. 2022

IJEER

View full text Add to dashboard Cite

show abstract

Anomaly Based Intrusion Detection through Efficient Machine Learning Model

Ugale

Potgantwar

2023

IJEER

View full text Add to dashboard Cite

Machine learning is commonly utilised to construct an intrusion detection system (IDS) that automatically detects and classifies network intrusions and host-level threats. Malicious assaults change and occur in high numbers, needing a scalable solution. Cyber security researchers may use public malware databases for research and related work. No research has examined machine learning algorithm performance on publicly accessible datasets. Data and physical level security and analysis for Data protection have become more important as data volumes grow. IDSs collect and analyse data to identify system or network intrusions for data prevention. The amount, diversity, and speed of network data make data analysis to identify assaults challenging. IDS uses machine learning methods for precise and efficient development of data security mechanism. This work presented intrusion detection model using machine learning, which utilised feature extraction, feature selection and feature modelling for intrusion detection classifier.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Design of an Efficient Face Recognition system using Deep Learning Technique

Cited by 2 publications

References 14 publications

Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer Detection

Optimized Feature Selection and Image Processing Based Machine Learning Technique for Lung Cancer Detection

Anomaly Based Intrusion Detection through Efficient Machine Learning Model

Contact Info

Product

Resources

About