Enhancing The Explainability of Neural Network

Language: English

Published by LAP LAMBERT Academic Publishing Sep 2024, 2024

6208064341 / 9786208064341

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This item is printed on demand - Print on Demand Titel. Neuware -Artificial Intelligence (AI) driven by neural networks is crucial in many applications like recommendation systems, language translation, social media, chatbots, and spell-checking etc. However, these networks are often criticized for being 'black boxes,' raising concerns about their explainability, especially in sensitive domains like healthcare, autonomous driving etc. Existing methods to enhance explainability, such as feature importance, often lack clarity and interpretability.To address this, the Object-Oriented Neural Network for Improved Explainability (OONNIE) was developed. OONNIE uses object-oriented modeling to combine loss and connection weight for computing feature importance and integrates domain-specific rules through OOP's extendability. The model emphasizes algorithmic transparency by detailing every training step. Evaluated on XOR and XNOR functions, OONNIE shows promising results in feature importance, faster loss reduction, and improved predictions after integrating domain rules. This marks a significant contribution to explainable AI, making OONNIE a valuable tool for developing trustworthy AI systems.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 204 pp. Englisch. …

Seller Inventory # 9786208064341

Title
Enhancing The Explainability of Neural Network
Author
Enoch Arulprakash
Publisher
LAP LAMBERT Academic Publishing Sep 2024
Publication year
2024
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
6208064341
ISBN 13
9786208064341
Item weight
322 grams
Dimensions
220x150x13 mm

buchversandmimpf2000

Emtmannsberg, BAYE, Germany

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