Scope of the Work 

This book focuses on algorithms and models related to Artificial Intelligence, Big Data, and IoT used to monitor, assess and evaluate the students' during online examination. It provides the readers a broader and deeper understanding of several perspectives, dynamics, challenges, and opportunities of online examination systems using Artificial Intelligence, Big data, and IoT. This book majorly focuses on problems that can be solved through computer vision, video and audio streaming, class imbalance data, audio-to-text process, multi-modal and bi-modal aspects, hand-written strokes, text similarity, biomedical ethics and advancements in machine and deep learning algorithms for the effectiveness of online examination system. Simplified and unique IoT-based systems designed for online examination systems have also been discussed and explored to analyze the student's trustworthiness in the exam.

Tentative table of contents or list of topics

1. Online Proctoring Tools and Techniques

2. AI Assisted Students Online Proctoring System

3. Uni and Multi-modal Aspects in the online proctoring system

4. Deep learning enhanced online monitoring system during examination

5. Enhancing Online Proctoring with a Novel IoT-Based Audio-to-Text Processing Multi-Modal System

6. Handling streaming video data using Edge devices for students' activity monitoring during online examination

7. Psychological & Ethical Aspects of Using Intelligent Systems in Online Proctoring

8. Deep Learning CNN Model for Monitoring the Student Emotion during the Class

9. AI-based Measuring Text Similarity for Answer Evaluation in Educational Assessment, but not limited to

Important Dates

Abstract Submission : 29 February 2024 

Abstract Acceptance : 31 March 2024 

Full chapter Submission : 31 July 2024 

 Chapter Acceptance : 31 August 2024 

Final Chapter Submission : 30 Septmeber 2024 

Submission to Publsiher : 31 October 2024 

Editors