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Here is a guide to scholars for your AI research work:

 Topic Selection

• Classify Research Interests: Start your research by gaining and understanding specific domains in the field of artificial intelligence that fascinates your interest. It includes machine learning, natural language processing, robotics, computer vision, ethical considerations in AI, and other areas.
• Consult Existing Literature: A literature review should be carried out to classify gaps or areas which need further investigation.
• Feasibility and Impact: The topic must be possible to cover within the given time period of your dissertation and has meaning within the academic community.

Proposal Writing

• Plan the Problem: The problem statement, scope, and objectives should be mentioned clearly.
• Methodology: The methods and approaches you plan to use should be defined.
• Literature Review: Review the existing research related to your topic.
• Cautious TimeLine: A timeline should be provided for completing various milestones.
• Submit Proposal: Typically, to a committee for approval this proposal must be submitted.

Research Design and Planning

• Data Collection: The data you need should be decided, and how you can collect it.
• Tools and Technologies: The programming languages, software, and hardware you require should be selected.

Implementation and Experimentation

• Build and Test Models: Several AI topics, include coding algorithms, training models, and validating results.
• Data Analysis: The data collected at the time of your experiments shall be proved.
• Optimization: Carry on your approach to progress results.

Writing

• Overview: Background, objectives, and a brief summary of the dissertation that includes.
• Literature Review: Review of present works in your research area.
• Methodology: Detail your experiments, data collection methods, and analytical approaches.
• Results: Layout your findings, using tables, charts, and text.
• Discussion: Take your results, compare to existing work, and discuss suggestions.
• Conclusion: Summarize your findings, discuss limitations, and propose avenues for upcoming research.
• References: List out all the academic sources you’ve named in your dissertation.

Review and Edit

• Peer Review: Have peers or guides to review your work.
• Study: Make necessary revisions based on response.

Proposal and Defence

• Prepare for defence: Present your findings before a committee.
• Submit: Place the final version of your dissertation.

Publication

• To publish your findings in academic journals, conferences, or other platforms.

Potential Topics for an AI Dissertation

• Reinforcement Learning in Autonomous Vehicles
• Bias and Fairness in Algorithmic Decision Making
• AI for Climate Change Modelling and Solutions
• Ethical Implications of AI in Healthcare
• Natural Language Understanding in Chatbots
• Deep Learning for Medical Image Analysis
• AI-Enabled Cybersecurity Systems
• Real-Time Analytics in IoT Using AI
• Human-AI Collaboration in the Workplace
• Explainability in Machine Learning Models

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