Integrating multi-omics data, such as proteomics, metabolomics, and genomes, in precision health provides revolutionary insights into biological systems and individualized treatment plans. This chapter focuses on AI-powered techniques for merging multi-omics data, made possible by high-throughput instruments such as next-generation sequencing and mass spectrometry. Single-omic techniques, which frequently overlook intricate molecular interactions essential to comprehending the etiology of disease, are outperformed by multi-omics integration. By capturing non-linear relationships and hierarchical characteristics across genes, proteins, and metabolites, artificial intelligence (AI) techniques—including deep learning models like CNNs and RNNs—increase multi-omics analysis and improve illness treatment and prediction. Federated Multi-Modal Embedding Networks (FMEN) promote precision models across omics types by advancing multi-omics integration through collaborative learning while protecting data privacy.

Metaheuristics are dilemma-independent methods that are generalizedin a variety of problems. In the real world, various problems are solved using generalized dilemma-independent methods called Metaheuristics Computation. Metaheuristic Nature Inspired Computing (MNIC) is a generalized approach to solve NP-hard problems by taking inspirations from the behavior of mother biological nature and their characteristics. Mining of Association rule, Frequent Itemset and High Utility Itemset are strongly interrelated and developing in the field of Data Mining. Metaheuristic nature inspired computation was widely used for the mining association rules of frequentitemsets and high utility itemsets to address the high computation time and optimal solutions. While various articles have been written, there is no systematic review of contemporary metaheuristic nature inspired approaches used in Association Rule Mining (ARM), Frequent Itemset Mining (FIM) and High Utility Itemset Mining (HUIM). This paper explores recent literature on various metaheuristics nature inspired approaches used for ARM, FIM and HUIM.

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

Artificial intelligence (AI) has transformed early disease diagnosis, providing exceptional speed and accuracy. However, fairness and bias challenges remain in healthcare AI, leading to discriminatory outcomes and worsening existing health inequities. These biases often arise from data imbalances, model architecture, and decision-making processes, with socioeconomic factors exacerbating historical disparities. Curating inclusive datasets with diverse demographic and socioeconomic representation is essential. Techniques like adversarial debiasing and data augmentation improve equity, while algorithmic fairness methods, such as regularization and fairness constraints, ensure equitable treatment. Differential privacy protects sensitive attributes, and ensemble methods like fairness-aware boosting uphold accuracy. Ongoing innovation is crucial for developing transparent, fair, and accurate AI systems in healthcare.