This application paper introduces a transformative solution to address the labour-intensive manual report generation, data searching & report revision process in government entities. Traditional methods of data extraction, analysis, and graph creation for annual reports are not only time-consuming but also prone to human errors. To mitigate these challenges, we propose an innovative system leveraging Generative Artificial Intelligence (GenAI), with a specific focus on large language models (LLMs). Our solution incorporates automated data extraction from diverse sources designated as internal knowledge base, text analysis, summarization using advanced language models, and the generation as well as revisions of informative graphs. Different LLMs like Google's Gemini Pro and OpenAI's GPT 4.0 has been used to read different data visualisation graphs and fetching the information from internal knowledge base, respectively, to update the graphs in automated manner. Solution implementation using Python language on the World Economic Situation and Prospects report by Department of Economic & Social Affairs, United Nation shows that the early result produces almost 0.87% to 17.46% average error rates in the task of factual data visualisation graph. Key benefit of this approach include improved time efficiency, consistency in report format, enhanced insights, and a user-friendly interface. A review and approval workflow facilitate user feedback, contributing to continuous model performance improvement.

Contextual understanding is a key aspect for learning a new domain through web search more effectively for making informed decisions. And with advent of machine learning approaches, it becomes even more fast and robust that enable collaboration between machine algorithms and humans. However, human expertise still holds the key for new domain, which has been proposed in this study as a key step in unsupervised learning approach of k-means clustering technique. Domain search term and context terms for the new domain are added to the clustering technique, and the relevance of the resultant groups has been tested. Context setting helps to analyse and understand the content of documents and other sources of information. For a new domain like Algorithmic Government, which does not have many documents on the web, it was found that contextual learning was up to 40% more relevant than the normal learning approach. The qualitative aspect of the clusters was found much better by the experts than quantitative aspect due to availability of lesser number of search documents. It was found that scientific research also supports the groups formed during contextual learning approach. This approach should help government to better understand and respond to the needs and concerns of their citizens by deriving better data insights in quick time, and to make more informed, evidence-based decisions, and sensitive to the needs and values of different communities and stakeholders. And thus, many stakeholders in the new domain can use this approach for exploration, research, policy formulation, strategizing, implementing and testing the various learnt concepts. A total of 15 search engines were used in the experimental settings with thousands of web crawling being done using Carrot 2 engine. Text embedding was done using bag-of-word technique and k-means clustering was implemented for producing 25 clusters across the two types of learnings.

Traditionally, performance measures such as accuracy, recall, precision, specificity, and negative predicted value (NPV) have been used to evaluate a classification model’s performance. However, these measures often fall short of capturing different classification scenarios, such as binary or multi-class, balanced or imbalanced, and noisy or noiseless data. Therefore, there is a need for a robust evaluation metric that can assist business decision-makers in selecting the most suitable model for a given scenario. Recently, a general performance score (GPS) comprising different combinations of traditional performance measures (TPMs) was proposed. However, it indiscriminately assigns equal importance to each measure, often leading to inconsistencies. To overcome the shortcomings of GPS, we introduce an enhanced metric called the Weighted General Performance Score (W-GPS) that considers each measure’s coefficient of variation (CV) and subsequently assigns weights to that measure based on its CV value. Considering consistency as a criterion, we found that W-GPS outperformed GPS in the above-mentioned classification scenarios. Further, considering W-GPS with different weighted combinations of TPMs, it was observed that no demarcation of these combinations that work best in a given scenario exists. Thus, W-GPS offers flexibility to the user to choose the most suitable combination for a given scenario.

Altcoins are alternative types of coins under cryptocurrency, apart from traditional Bitcoins, for which predicting the price movement presents a multifaceted challenge deeply rooted in the volatile nature of the cryptocurrency market. This study compares and analyzes different Machine Learning (ML) and Deep Learning (DL) models for price movement prediction through diverse data sources like Bitcoin prices, social media sentiments, and news sentiments, apart from different socio-economic factors specific to USA geography due to its maturity on use of Altcoins, with temporal scope spanning from 2016 to 2022 collating over 77 M tweets and news items. Ethereum, Binance, XRP, Cardano, Monero, Tron, Stellar, and Litecoin, were considered for experimentation across widely used algorithms like Gradient Boosting, Naive Bayes, Decision Trees, Neural Networks, and the like, with different day-length lags ranging up to 4 days. Highly relevant features were selected using Random Forest selection method and highly correlated features have been removed before the modeling. Accuracy for price movement prediction models varied from 71.03% for Ethereum to 66.14% for Stellar, which were better by 15-20% as compared to percentage benchmarking done by literature to be ranging around 50 s and 60 s. For the model validation, sensitivity analysis involving day-wise lag analysis, and different data splits (based on size and months) were considered, which was stable for the high performing models. Further, an interesting result was observed during the study. In order of priority, Bitcoin prices, social media sentiments, and news sentiments significantly impact altcoin price movement. This implies that by studying the Bitcoin price movement and market sentiments, investors can make wise decisions towards altcoin investments. This study holds significance for researchers and practitioners to understand the impact in the trading market of cryptocurrency and help an investor diversify their portfolio. The findings will be helpful for Algo Trading Platforms, Financial Advisors, Trading Experts, Industry Experts, Researchers, and Scholars.

The book provides various EdgeAI concepts related to its architecture, key performance indicators, and enabling technologies

We begin this chapter by proposing a conceptual framework which helps to determine whether it is feasible and beneficial to adopt EdgeAI in a particular application of Algorithmic Government. Next, we discuss challenges in edge computing which include Network Integration and Resource Management, Cloud and Edge Coexistence and Reliability of Edge Devices. Further, we talk about ethical issues in AI and EdgeAI specifically, and several policies and guidelines which aim at addressing these problems. Finally, we discuss technological implications of adopting EdgeAI followed by emerging hardware devices which facilitate EdgeAI applications.