Computer Science Dissertation Topics

Info: 949 words (1 pages) Computer Science Dissertation Topics
Published: 21th May 2025 in Computer Science Dissertation Topics

Related Services

Our academic writing and marking services can help you!

Designing Fairness-Aware AI Algorithms to Profile Risk for Insurance Industry

Research Gap:

AI risk assessment models often bring preconceived biases from datasets, resulting in unfair pricing or claims refusals.

Objectives:

• Design and implement a fairness aware AI model for dynamic insurance pricing.
• Use bias detection metrics, and apply mitigation measures (i.e., pre-processing, in-processing, post-processing).
• Assess effectiveness of model, under trade-offs between fairness and accuracy.

Methodology:

• Use synthetic datasets, or open insurance datasets.
• Implement using Python (Scikit-learn, AIF360).
• Quantitatively assess biased vs. de-biased models using fairness metrics (Demographic Parity, Equal Opportunity).

Theoretical Support:

Fairness in Machine Learning, Algorithmic Bias (Barocas et al., 2016).

Key References:

• K. Kuppan, D. Bhaskar Acharya and D. B, “Foundational AI in Insurance and Real Estate: A Survey of Applications, Challenges, and Future Directions,” in IEEE Access, vol. 12, pp. 181282-181302, 2024, doi: 10.1109/ACCESS.2024.3509918. https://ieeexplore.ieee.org/abstract/document/10772203/authors#authors

• Mehrabi, F. Morstatter, N. Saxena, K. Lerman and A. Galstyan, “A survey on bias and fairness in machine learning,” ACM Computing Surveys (CSUR), vol. 54, no. 6, pp. 1–35, 2021.

• R. Zemel et al., “Learning Fair Representations,” in Proc. ICML, 2013

Real-time insurance fraud detection with Edge AI and Federated Learning

Research Gap:

Existing models for fraud detection almost exclusively rely on fully centralized data, creating data privacy issues and leading to long delays when detecting fraud.

Objectives:

• To design an AI model that is compatible with edge-based systems for the intended purpose of detecting fraud in vehicle or health insurance.
• Investigate using federated learning which will allow organizations to learn on their own data without the direct sharing of raw data.

Methods:

• Building an edge-based environment (such as mobile telematics).
• Use frameworks like TensorFlow Federated and PySyft.
• Assess the model’s accuracy, latency and communications constraints.

Theoretical support:

Federated Learning (Google, 2017), Edge AI, Adversarial ML risk evaluation.

Key References:

• K. Kuppan et al., 2024.

• Q. Yang, Y. Liu, T. Chen and Y. Tong, “Federated Machine Learning: Concept and Applications,” ACM Transactions on Intelligent Systems and Technology, vol. 10, no. 2, pp. 1–19, 2019.

• Y. Kang, Y. Li and Z. Xu, “Edge Computing for Real-Time AI Applications,” IEEE Internet of Things Journal, vol. 7, no. 2, pp. 1564–1574, Feb. 2020.

An NLP-Based Legal Compliance Review for Real Estate Contracts

Research Gap:

Contract review of real estate transactions is often a manual and error-prone process, yet compliance auditing using AI remains scarcely explored.

Goals:

• Build an NLP model to identify compliance risks in property leases or contracts.

• Automate clause detection and red-flagging using rules for regulations (e.g., tenancy, zoning).

Method:

• Fine-tune pre-trained models (BERT, LegalBERT) on labeled legal documents datasets.

• Apply rule-based post-processing for tagging by contract-specific.

Tools:

Python, Hugging Face Transformers, SpaCy.

Theoretical support:

Legal NLP, Automation of Compliance, Document Intelligence.

Key IEEE References:

• K. Kuppan et al., 2024.

• D. Chalkidis et al., “Legal-BERT: The Muppets straight out of law school,” in Proc. Findings of EMNLP, 2020.

• M. Zhong, H. Chen, C. Yu and H. Xie, “Smart Contract Semantic Analysis Based on NLP,” in IEEE Access, vol. 8, pp. 171448–171459, 2020.

Multimodal data for an AI-powered dynamic pricing system for real estate rentals

Research Gap:

Current models for pricing real estate rentals ignore multimodal signals (e.g., text, images and geographic data) when pricing real estate dynamically.

Objectives:

Develop a multimodal AI model that uses images of real estate rental properties, text descriptions and location to set a rental price at a point in time.
Extend this pricing signal to account for demand signals in the market that will vary continuously over time.

Method:

• Generate CNN outputs as image features; NLP outputs as features for the property description; integrate GIS data on location.

• Employ either gradient boosting or transformer-based regression for the value ultimately predicted.

Tools:

TensorFlow for deep learning, XGBoost for gradient boosting, OSM and possibly Zillow dataset.

Theoretical support:

Multimodal Learning, Dynamic Pricing Models, Explainable AI in pricing.

Key IEEE References:

• K. Kuppan et al., 2024.

• C. Zhang, J. Sun, Y. Qi and X. Hu, “A survey of dynamic pricing: From the perspective of machine learning,” IEEE Access, vol. 8, pp. 187212–187228, 2020.

• L. Wei, H. Zhou and J. Huang, “Multimodal Learning for Property Value Estimation,” in Proc. IEEE International Conference on Big Data, 2019.

Explainable AI for Automated Property Valuation via Computer Vision

Research Gap:

While property valuation systems are black boxes and seem not open to scrutiny, they are not adopted in regulated real estate businesses.

Objectives:

• Develop a computer vision model for property valuation that uses visual traits.

• Develop a model that incorporates explainability (Grad-CAM, SHAP), that can substantiate their predictions.

Method:

• Using labeled interior/exterior images, with appraisal values.

• Train a regression-based CNN or ViT model.

• Provide a visualization of what areas of the image most contributed to the prediction for the price.

Tools:

PyTorch, SHAP, Grad-CAM, Label Studio (for annotation).

Theoretical support:

Explainable AI (XAI), Visual Interpretability in CV.

Key IEEE References:

• K. Kuppan et al., 2024.

• R. R. Selvaraju et al., “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,” in IEEE ICCV, 2017.

• S. Lundberg and S.-I. Lee, “A Unified Approach to Interpreting Model Predictions,” in Advances in Neural Information Processing Systems (NeurIPS), 2017. (SHAP)

Constructing a Privacy-Preserving AI System for Smart Building Energy Management

Research Gap:

Real-time energy optimization of smart buildings could potentially infringe upon the user´s privacy as the data is stored in a centralized manner.

Objectives:

• Build a privacy preserving AI based solution for smart energy management.

• Use federated learning with differential privacy tech to protect the data.

Methodology:

• Simulate multi-zone building with sensory inputs (HVAC, Lighting).
• Application of FL (with local training updates) with DP-noise.

Tools:

TensorFlow, Smart Building Simulator, PySyft, DP-SGD.

Theoretical support:

Smart Building AI, Differential Privacy (Dwork et al.), IoT energy optimization.

Key IEEE References:

• K. Kuppan et al., 2024.

• C. Dwork, “Differential Privacy,” in Automata, Languages and Programming, Springer, 2006.

• T. Li, A. K. Sahu, A. Talwalkar and V. Smith, “Federated learning: Challenges, methods, and future directions,” IEEE Signal Processing Magazine, vol. 37, no. 3, pp. 50–60, 2020

Study Resources

Free resources to assist you with your university studies!

Computer Science Topics