## 2 Summary of the Contributions
The system's performance is robust, achieving accurate recognition even under natural conditions with varying speeds and lighting conditions. Key contributions include the development of a practical gesture recognition methodology for VLC, utilizing off-the-shelf components, and demonstrating the effectiveness of a single photo-diode receiver setup. The system's accuracy and efficiency are notable, allowing for gesture recognition within a communications-based VLC system. Possible future directions involve increasing the number of recognizable gestures, exploring gesture recognition from different aspects of sunlight and placements, and incorporating the system into automated VLC setups for more versatile applications. The study's results highlight the potential of this approach for various domains such as healthcare, commerce, and home automation. Paper [6] focuses on analyzing and modeling road traffic accidents (RTAs) using ML classifiers. The aim is to assist transportation authorities and policymakers by developing predictive models for RTAs. The research utilizes a real-life RTA dataset from Gauteng, South Africa, and evaluates the performance of various ML classifiers including naive Bayes, logistic regression, k-nearest neighbor, AdaBoost, support vector machine, and random forest. The study also includes dimensionality reduction techniques and multiple missing data methods. The findings of the study show that a random forest (RF) classifier combined with multiple imputations by chained equations (MICE) for handling missing data achieves the best overall performance. RF consistently outperformed other classifiers in terms of accuracy, precision, recall, and AUC. Additionally, the study found promising results with linear discriminant analysis (LDA) for dimensionality reduction. The study acknowledges its limitations, such as the use of a dataset from a specific region and the exclusion of certain features. Future work could involve hyperparameter tuning for specific classifiers, testing other ML algorithms like artificial neural networks and deep learning, and expanding the analysis to different datasets or regions. In [7], a robust financial crisis early warning model for Chinese listed companies is proposed. The study's premise stems from the economic challenges and risks that these companies face, driving the need for effective risk prediction and mitigation strategies. The authors propose a unique approach by incorporating textual data analysis, specifically the sentiment and tone analysis of financial news texts and the management discussion and analysis (MD&A) sections in annual reports. By leveraging web crawling and textual analysis techniques, the emotional tones of these texts are extracted. These tones serve as supplementary indicators alongside traditional financial indicators, providing insights into internal and external aspects of listed companies. The study includes 1082 Chinese A-share listed companies from 2012 to 2021 as its sample. This research systematically evaluates the impact of emotional tone indicators on the accuracy of financial crisis early warning models. Thirteen ML models are employed to predict financial crises, and their performance is assessed using various evaluation metrics. The findings highlight several key points, such as textual data as indicators, model comparisons, external vs. internal information, and implications and future directions. The findings underscore the potential of emotional tone analysis of financial news in improving early warning models. This approach helps mitigate risks and increases the accuracy of predictions for listed companies. Furthermore, the study suggests avenues for future research, such as exploring the impact of linguistic features beyond emotional tone and developing specialized comprehensive emotional dictionaries for financial texts. Paper [8] explores the field of DNA-based informatics, focusing on the construction and operation of computers using DNA as both hardware and software.