Advances in computational intelligence and robotics book series

The development of urban infrastructure for optimal energy use is very important for economic growth of any country which can benefit from recent advances in intelligent technologies. The combined use of the Internet of Things (IoT) and Artificial Intelligence (AI) plays a crucial role in cities to monitor and manage their energy use. These tools allow for real-time observation, control, and adju…

Traditional real estate services and freelance platforms suffer from high fees, centralized control, biased dispute resolution and limited transparency. This chapter proposes an AI-driven, blockchain-based decentralized system that tokenizes real estate assets and digital project outputs enabling secure, trustless commerce without intermediaries. Machine learning enhances the ecosystem through au…

Artificial Intelligence (AI) has emerged as a powerful driver of economic growth, innovation and productivity. However, its benefits are not automatically shared equally across societies and rapid technological change can exacerbate inequalities, disrupt labor markets and create environmental challenges. This chapter explores how AI can be harnessed to achieve sustainable and inclusive growth, ba…

This review chapter explores how modern data-driven methods can deepen our understanding of unstable financial environments. The chapter focuses on the combined use of wavelet transforms and stochastic modeling to analyze complex, irregular, and rapidly changing financial signals across multiple time scales. It examines how Machine Learning techniques enhance these models by improving pattern rec…

This chapter presents a strategy to improve the digital wallet (DW)'s security system by utilizing Google Authenticator (GA), Asmuth–Bloom Secret Sharing (ABSS), and HashiCorp Vault (HV) in combination with Amazon Web Services (AWS). By protecting keys and passwords, GA is recommended as a means of enhancing DW security. We utilized HV's encryption, Kubernetes authentication (KA), and AWS Identit…

The chapter examines the interconnection between Artificial Intelligence (AI) and the sustainable investment sector in India, with emphasis on the different avenues that are coming up and the bothers that continue to ban. It gives an outline of the current situation in the realm of green finance, which includes issues like green bonds, ESG funds, and all the different expressions of law-driven ec…

The rapid growth of deep learning (DL) workloads has increased the need for specialized hardware accelerators that deliver high performance, energy efficiency, and scalable deploy-ment. This chapter examines the roles of graphics processing units (GPUs), field-programmable gate arrays (FPGAs), and application-specific integrated circuits (ASICs) in ac-celerating contemporary DL models across data…

Supply chain management systems, healthcare, payments, businesses, the IoT voting systems, and countless more utilize its high security and transparency to protect a variety of digital assets from In order to demonstrate how blockchain technology functions overall, this chapter also provides a number of examples of its use and the difficulties associated with implementing it in other sectors. Vir…

The retail industry is undergoing a transformation due to the integration of Artificial Intelligence (AI) in promotional strategies. Traditional methods like print ads, radio commercials, and in-store displays face challenges in today's dynamic retail environment, such as the inability to integrate with online platforms and provide interactive experiences. AI technologies like machine learning, n…

Financial markets exhibit nonlinear, non-stationary, and multi-scale dynamics that challenge classical econometric models and modern deep learning approaches. Traditional methods offer interpretability but lack flexibility under regime shifts and cross-scale interactions, while deep neural networks improve predictive accuracy at the cost of transparency, raising concerns about governance and syst…

AI is changing the way businesses hire, train, keep an eye on, and manage their workers, which is changing the way people work in many fields. AI-powered solutions make hiring more efficient by using automated screening, predictive hiring, and data-driven talent analytics. AI-driven automation and augmented intelligence are changing job responsibilities in the workplace, leading to the creation o…

Urban economies face growing vulnerability to systemic crises including pandemics, climate shocks, and financial disruptions. Conventional recovery models—often reactive and siloed—have proven insufficient, creating a resilience deficit. Artificial intelligence (AI) and predictive analytics are increasingly positioned as tools to strengthen anticipatory governance and adaptive planning. This pape…

The automated prediction of macroeconomic index shifts has emerged as a critical research area with the integration of advanced Artificial Intelligence (AI) and Machine Learning (ML) methodologies.This study develops a robust analytical framework for forecasting variations in key macroeconomic indicatorssuch as Gross Domestic Product (GDP) growth, inflation dynamics, interest rate fluctuations, a…

This chapter looks at how AI techniques including ML, DL, NLP and RL can be used in many fields, such as finance, operations, cybersecurity, and healthcare. It shows how AI makes predictive analytics better, speeds up decision-making, and makes dynamic response systems better while also cutting down on human mistakes and bias. It also talks about ethical and governance issues like accountability,…

The central theme of the chapter consists of integration of Environmental, Social, and Governance concepts into investment analysis as the world faces significant environmental and societal threats. The goal is to bridge the sustainable development financing gap and integrate high technologies such as navigate non-linear complexities. Technologies such as Machine Learning can be applied in bankin…

In today's complex and uncertain world, crises such as pandemics, financial crashes, climate shocks, and cyber-attacks are becoming more frequent, interconnected, and harder to predict. Traditional crisis management, based on linear human judgment, cannot keep pace with fast-moving, data-intensive emergencies. AI and ML offer new capabilities to forecast, prevent, and manage crises through early-…

Crude Oil, as a vital component of the world economy, requires precise price forecasting in order to control investment risks and maintain economic planning. Six supervised machine learning models—Decision Tree Regression (DTR), K-Nearest Neighbors (KNN), Multiple Linear Regression (MLR), Random Forest Regression (RFR), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP)—are used in…

The emergence of Industry 5.0 marks a new chapter in industrial development. Instead of focusing mainly on automation and efficiency, like Industry 4.0, this new phase brings people back to the center of progress. The goal is not only to use advanced technologies but to pair them with human intelligence, creativity, and values. With this shift, Industry 5.0 encourages a future that supports socia…

The current world economies are in a highly volatile framework characterized by thick interdependencies, and quick changing risk factors. The classical econometric models with their assumption of the stasis and restrictive data granularity cannot predict systemic shocks and timely interventions. This chapter provides a technical review of how Artificial Intelligence can increase economic resilien…

Predicting financial markets has become increasingly relevant with the significant growth in financial risk levels and uncertainties. This paper aims to forecast three major cryptocurrencies (Bitcoin, Ethereum, and Litecoin) through commodity submarkets during the period 2015--2023, covering the COVID-19 crisis and Russian-Ukrainian crisis periods. In a forecasting target based on long short-term…