# AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning
## 5 Case Studies: Success Stories and Lessons Learned
### Finance and Accounting  
In the finance sector, the study by [PERSON] (2024) [121] investigates the transformative impact of AI and ML on portfolio management. The research highlights how these technologies revolutionize investment strategies by enabling more precise data analysis and prediction capabilities. AI-driven techniques such as reinforcement learning, natural language processing, and sentiment analysis are employed to develop dynamic and adaptive investment strategies. These methods allow continuous portfolio adjustments in response to real-time market conditions, optimizing asset allocation and risk diversification. The integration of AI not only enhances the ability to predict market trends with unprecedented accuracy but also improves overall efficiency and cost-effectiveness of investment strategies. The review by [PERSON] (2024) [122] explores AI applications in fraud detection within accounting. ML algorithms identify patterns and anomalies in financial data that could indicate fraudulent activity. Supervised learning models are trained on labeled datasets to distinguish between fraudulent and non-fraudulent transactions, while unsupervised learning models detect anomalies without prior labeling. Natural language processing (NLP) analyzes textual data like emails and financial documents to uncover suspicious activity and hidden relationships, particularly useful in forensic accounting. Case studies demonstrate AI's potential to enhance the accuracy, efficiency, and effectiveness of fraud detection efforts in accounting. Energy Sector  
The study by [PERSON] (2023) [113] explores integrating prior knowledge with data-driven methods to enhance ML robustness in energy-efficient building engineering. Employing a component-based machine learning (CBML) approach, the research encodes building structural knowledge as semantic information within the model, allowing for more efficient data utilization and improved model robustness. The method significantly enhances ML model performance, even with sparse data inputs, by improving their ability to generalize from small and inconsistent datasets. The approach reduces training time and increases interpretability, making models more practical for real-world applications. The research \"Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain\" by [PERSON] (2022) [112] investigates using satellite imagery combined with ML to estimate poverty levels. The study emphasizes the critical role of domain knowledge in enhancing model transparency and explainability. By integrating domain expertise, the research aims to improve the interpretability of ML predictions, essential for gaining acceptance and trust within the development community. The findings suggest domain knowledge is vital for achieving scientific consistency and for broader dissemination of these technologies in poverty measurement, bridging the gap between complex ML models and practical applications in poverty and welfare. Exoskeleton Devices  
The study by [PERSON] (2024) [123] explores integrating AI to improve human-machine interaction in medical exoskeletons. AI technologies, including ML, NLP, and predictive analytics, significantly enhance the efficiency and comfort of these devices. Machine learning algorithms analyze sensor data in real time, optimizing control strategies to adapt to user movements, improving user comfort and rehabilitation outcomes. NLP facilitates intuitive control through voice commands, reducing cognitive and physical burden on users. Predictive analytics anticipates user needs, enhancing exoskeleton responsiveness and minimizing error risk. Case studies of AI-integrated exoskeletons like the EksoGT and ReWalk demonstrate improved rehabilitation outcomes and user satisfaction. Explainable AI (XAI)  
The study by [PERSON] (2024) [104] presents a novel approach to integrating domain knowledge with XAI through a dialogue system.