Showing posts with label binary. Show all posts
Showing posts with label binary. Show all posts

Saturday, 1 March 2025

John Lyly’s Techniques of Character Portrayal in Endymion | Chapter 7 | An Overview of Literature, Language and Education Research Vol. 8

This chapter analyzes the characters and examines the characterization techniques John Lyly employs in his euphuistic court play Endymion: The Man in the Moon (1591). From the moon to the earth, the characters are arranged in a hierarchy and acquire mythological, metaphorical, and historical importance. The author employs a number of strategies, such as the conventional approaches to speech and action methods of characterization. He also downplays the use of action to portray characters because the play's events fall short of the Aristotelian concept of a plot, which was popular throughout the Renaissance. However, he pays close attention to how the characters are portrayed through their utterance. This results from the fact that the characters spend most of the time talking about their attitudes, their relationships, and the few events that take place in the play. Additionally, Lyly employs the less conventional strategies of social positioning and juxtaposition. Endymion's binary nature and the abundance of antagonistic character pairs that abound in the play generate the method of characterization by juxtaposition. Characters are portrayed also using the social positioning approach, which positions them in a layered social, allegorical, and mythical structure. By fitting into the designated layers of the structured structure, the characters acquire a variety of collective traits. The chapter demonstrates the creativity of Lyly's dramaturgy in character depiction despite the absence of a massive plot that abides by the Aristotelian rules. The chapter is going to take a close textual analysis of the characters and induce the techniques that the playwright uses in their portrayal.

 

Author (s) Details

 

Mufeed F. Al-Abdullah
Department of English, Faculty of Arts, Jerash University, Jordan.

 

Please see the book here:- https://doi.org/10.9734/bpi/aoller/v8/3468

Monday, 16 October 2023

Data Bias Detection in Machine Learning Using Various Statistical Measures | Chapter 11 | Advances and Challenges in Science and Technology Vol. 7

 The study explains numerous mathematical techniques for bias labeling. The techniques are secondhand with twofold and multinomial dafigta. Artificial intelligence (AI) is a broad term referring to machines or calculatings that mimic human intelligence. Machine learning (ML), a subdomain of up-to-date AI, creates rules established training dossier. The Bias detection of dossier and model are essential in today’s scenarios. The lightened data will give better forecast results. Good predictions help institutions to take better decisions.

Author(s) Details:

Harshaprabha N. Shetty,
Accenture Technology, Bengaluru, India.

Sony Asampalli,
Accenture Technology, Hyderabad, India.

Prabhu Vara Prasad Bonam,
Accenture Technology, Hyderabad, India.

Please see the link here: https://stm.bookpi.org/ACST-V7/article/view/12208