Artificial Intelligence (AI) has impacted global economy, workforce productivity, smart health, smart cities, smart transport, and much more to come. Large Language Models (LLM) such as ChatGPT and Google's Gemini, have been widely adopted in various applications. Blockchain Technology stands as a towering disruptor in today's tech landscape, offering assurances of enhanced security and scalability for various applications. Within the realm of healthcare, its adoption has surged, spanning from streamlined recordkeeping to bolstered clinical trials, fortified medical supply chains, and vigilant patient monitoring. These applications harness the intrinsic attributes of blockchain to elevate standards of safety, privacy, and security within the healthcare sector. The combined power of AI and blockchain has the potential to revolutionize healthcare delivery, ensuring improved security, transparency, and efficiency. Nevertheless, Porru et al. [1] have highlighted deficiencies in the processes, tools, and techniques within this domain. Hence, this paper aims to furnish a structured framework that ensures both security and sustainability in the development of healthcare blockchain applications. This paper also provides an overview of societal impact on both technologies. This article has evolved best practice guidelines and a systematic development framework for AI-Blockchain integration, known as AI-BlockchainOps. This research has also developed a reference architecture, exemplifying the modeling of an Electronic Health Record (EHR) using BPMN and simulation. Within this Electronic Health Record (EHR) scenario encompassing 100 user requests, the simulation absorbed 97.09% of cloud resources, with 76.33% allocated to knowledge discovery, and a utilization rate of 93.20% for blockchain scientists, alongside various other contributing factors.

The quadratic spline is used in the conventional Levin's method to evaluate the oscillatory integral. Generally, the Levin method requires O ( n 3 ) computations and can be unstable. Here, the quadratic spline interpolation method requires solving recurrence relations of the derivatives of the given function and needs only O ( n 2 ) computations, where ( n ) is the number of selected nodes. The recurrence relations for large (n) are shown to be not ill-conditioned. Linear piecewise and cubic interpolation do not offer such advantages. The bound on the solution is obtained in terms of frequency. Numerical examples, including an application to a scattering problem, adequately illustrate the performance of the proposed method. They exhibit stability when the nodes are adequately large, unlike the conventional Levin method.

An innovative solution created to solve the safety issues involving children is the Child SafetyMonitoring System based on IoT.This system allows real-time monitoring and tracking of kids, assuring their safety in varied surroundings by utilizing the Internet of Things' capability.Parents or other adults who are responsible for children may continually monitor their whereabouts, activities, and vital signs by integrating IoT-enabled sensors and gadgets.The technology uses geo fencing to create secure zones and borders, immediately alerting carers if a youngster leaves these marked bounds.The system makes use of data analytics to offer insightful information on a child's routine, behavior, and general health.By detecting possible dangers and enhancing safety procedures with the use of this information, proactive steps may be performed.In.The technology immediately warns parents or guardians in case of emergency, allowing for quick response and intervention.The system also makes it easier for parents, carers, and educational institutions to collaborate and communicate with one another, ensuring that child safety is taken seriously.The IoT-based kid Safety Monitoring System provides improved parental control, customization, and customization to cater to the unique demands of every family or kid.Even when parents are physically apart from their child, it offers remote surveillance and peace of mind.The system prioritizes privacy and data security, is scalable, adaptive, and ensures the accuracy and confidentiality of personal data.

Artificial intelligence (AI) and machine learning (ML) applications are applied in many applications and devices, and it is expected to grow by 15 trillion dollars by 2030. There are more demands for explainable AI (XAI) for its improvement in explainability attributes of the AI quality. Software quality is defined as the product meets its required product specification and is expected to behave as it is expected by the stakeholders. Furthermore, we need a systematic approach to the design, development, implementation, and testing of AI products. Therefore, this chapter proposes a software engineering framework for AI and ML applications (SEF - AI and ML) supporting the complete XAI application development phases including a reference architecture to standardize across XAI applications. The framework has been validated through a case study involving an explainable Chatbot using business process modeling notations (BPMN), modeling, and simulation. The results demonstrate a 98% utilization rate and improved time efficiency, confirming the validation of performance and resource requirements for cloud-driven AI Chatbot services. Therefore, SEF - AI and ML has the potential to be a standard framework for AI and ML applications to achieve the desired quality and certainty of AI products and services.