BHAVANA EDHARA

Proactive Risk Analyst with extensive experience in identifying and mitigating potential threats through comprehensive research and analysis. Skilled in data analysis, Python programming, and enhancing cybersecurity measures, offering insightful recommendations to improve decision-making and reduce financial losses. Strong collaborative and teaching abilities, focused on advancing risk control and mitigation strategies.

Title: Early detection of dysgraphia in school children using deep learning.

Description: This study proposes a seep learning-based model to detect dysgraphia( a neurological writing disorder) in school children aged 7-12.

The model uses handwritten data sets and algorithms like CNN and random forest to identify writing difficulties early, enabling timely interventions and support.

Title:

Detecting phishing attacks using natural language processing and machine learning

Description:

The project aimed to detect phishing attacks by analyzing URL structures using NLP and ML, without accessing website content. It classified websites into benign, spam, and malicious categories based on lexical features like URL length and presence of security-sensitive words. Supervised learning algorithms(random forest, support vector machine) were used for classification. Host-based features and traffic rank were considered for accuracy enhancement.

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