This systematic review critically examines the utilization of machine learning (ML) and deep learning (DL) techniques in detecting and predicting Dysgraphia through handwriting recognition. The primary research objective is to systematically assess the significance, contributions, performance, and limitations of recent technological advances in identifying dysgraphic patients. Employing a structured search and selection methodology based on the PRISMA protocol, 34 peer-reviewed studies from key academic databases, including IEEE Xplore, PubMed, Scopus, Google Scholar, and Science Direct, were comprehensively analyzed. The review methodology focused extensively on data collection methods, ethical considerations, preprocessing approaches, feature extraction and selection techniques, and the performance metrics of various ML models in dysgraphia prediction. Our key findings indicate that a Hybrid AI method combining convolutional neural networks and support vector machines demonstrates high accuracy rates, reaching up to 99.33%. This systematic review compares machine learning and deep learning use in diagnosing Dysgraphia through handwriting recognition with traditional cognitive evaluations, highlighting the latter's lack of scalability and high labour demands. Additionally, this review highlights that research on Dysgraphia has mainly focused on Latin scripts, overlooking the unique challenges of non-Latin scripts. It stresses the importance of including diverse linguistic data for more accurate global diagnoses. Additionally, Dysgraphia is influenced by cognitive, motor, and language skills, underscoring the need for multimodal data to enhance early detection and treatment effectiveness. The paper advocates for research that integrates various data types and expands linguistic diversity in training datasets, aiming to improve the diagnostic accuracy and global applicability of dysgraphia detection tools.

This study discusses how financial economic theory and its quantitative tools can be applied to create socioeconomic indices and develop a financial market for the so-called “socioeconomic well-being indices”. In this study, we quantify socioeconomic well-being by assigning a dollar value to the well-being factors of selected countries; this is analogous to how the Dow 30 encapsulates the financial health of the US market. While environmental, social, and governance (ESG) financial markets address socioeconomic issues, our focus is broader, encompassing national citizens’ well-being. The dollar-denominated socioeconomic indices for each country can be viewed as financial assets that can serve as risky assets for constructing a global index, which, in turn, serves as a “market of well-being socioeconomic index”. This novel global index of well-being, paralleling the Dow Jones Industrial Average (DJIA), provides a comprehensive representation of the world’s socioeconomic status. Through advanced financial econometrics and dynamic asset pricing methodologies, we evaluate the potential for significant downturns in both the socioeconomic well-being indices of individual countries and the aggregate global index. This innovative approach allows us to engineer financial instruments akin to portfolio insurance, such as index puts, designed to hedge against these downturn risks. Our findings propose a financial market model for well-being indices, encouraging the financial industry to adopt and trade these indices as mechanisms to manage and hedge against downturn risks in well-being.

Despite the potential importance of crime rates in investments, there are no indices dedicated to evaluating the financial impact of crime in the United States. As such, this paper presents an index-based insurance portfolio for crime in the United States by utilizing the financial losses reported by the Federal Bureau of Investigation. The objective of our paper is to introduce new risk hedging financial contracts for crime, consistent with dynamic asset pricing. Underlying the index, we hedge the investments by issuing marketable European call and put options and providing risk budgets. These budgets show that real estate, ransomware, and government impersonation are the main risk contributors in our index. Next, we evaluate the performance of our index via stress testing to determine its resilience to economic crisis. Of all the factors considered in this study, unemployment rate has the potential to demonstrate the highest systemic risk to the portfolio. Our portfolio will help investors envision risk exposure in the market, gauge investment risk based on their desired risk level, and hedge strategies for potential losses due to economic crashes. In conclusion, we provide a basis for the securitization of insurance risk from certain crimes that could forewarn investors to transfer their risk to capital market investors.

Bridging the gap between theoretical knowledge and practical application remains challenging in big data education. Our paper proposes a platform where students actively participate in a role-based learning (RBL) exercise designed to simulate real-world big data projects. This RBL exercise assigns each student participant to a business analyst role tasked with eliciting requirements or a customer role that provides those big data requirements. We conducted RBL sessions for postgraduate and undergraduate students learning big data analytics/data warehousing. Through role-playing in a collaborative environment, students face challenges such as unclear objectives, data privacy concerns, and evolving requirements. We evaluated the effectiveness of RBL simulations through brainstorming sessions, which verified that the students achieved the learning outcomes through RBL exercises. We also collected students’ feedback through a survey and found the RBL experience helped us understand the big data requirement elicitation skills, highlighting the significance of communication and collaboration skills. Further, we have evaluated students through a final exam question, and identified that students who participated in the RBL exercise outperformed in the big data requirement elicitation question. In summary, our research demonstrates that this RBL approach offers a valuable learning experience by enabling students to directly experience the complexities of big data requirement elicitation and identify the future requirements or challenges that encourage them to acquire the required skills.

Natural disasters, such as tornadoes, floods, and wildfire pose risks to life and property, requiring the intervention of insurance corporations. One of the most visible consequences of changing climate is an increase in the intensity and frequency of extreme weather events. The relative strengths of these disasters are far beyond the habitual seasonal maxima, often resulting in subsequent increases in property losses. Thus, insurance policies should be modified to endure increasingly volatile catastrophic weather events. We propose a Natural Disasters Index (NDI) for the property losses caused by natural disasters in the United States based on the "Storm Data" published by the National Oceanic and Atmospheric Administration. The proposed NDI is an attempt to construct a financial instrument for hedging the intrinsic risk. The NDI is intended to forecast the degree of future risk that could forewarn the insurers and corporations allowing them to transfer insurance risk to capital market investors. This index could also be modified to other regions and countries.