Image processing has become more crucial in medical applications due to its ability to collect and evaluate data from medical images. This book chapter provides an overview of various image processing techniques used for medical applications, including deep learning algorithms, segmentation techniques, and a combination of both. Additionally, the authors discuss other studies that analysed X-rays and used image processing to identify cancer, brain tumours, and other disorders. The results of the study demonstrate how image processing techniques have the potential to significantly improve sickness detection speed and accuracy, facilitating early diagnosis and treatment. The planning of therapies and the accuracy of diagnoses can both be enhanced by the use of image processing tools. Healthcare workers' ability to recognise and manage a variety of medical conditions will undoubtedly increase.

A concept that has been shown to be valuable in one circumstance and is likely to be useful in others is known as a pattern. A pattern can be interpreted in a variety of ways, and each interpretation has its own particularizations that are suited to the particular form of the pattern it represents. The term “pattern” can be used to describe anything, like a group of items that function in tandem with one another. The analysis of these patterns is important in order to improve recognition. Finding patterns in data is the primary emphasis of the field of pattern analysis, which is a subfield of artificial intelligence and computer science that employs the usage of algorithms. In the context of a data stream, the term “pattern” refers to any underlying correlations, regularities, or structures. If it finds significant patterns in the data already stored, a system may be able to anticipate generating predictions based on fresh information arriving from a source that is analogous to the one it is currently using.

Electric vehicles (EVs) are becoming increasingly popular as an efficient transportation solution but they also present unique challenges for energy management. Bi-directional charging (BDC) is a solution that allows EVs to not only consume energy from the grid but also supply energy back to the grid. This facilitates vehicle-to-load (V2L) integration, where EVs can act as mobile power sources for homes, buildings, and the grid. V2L enables better energy management by utilizing EVs as a flexible resource to balance grid demand and supply in the proposed system. This is achieved through intelligent coordination between the EVs, charging stations, and the grid, using smart meters and communication networks. Integration of BDC and V2L also enables EVs to provide backup power during grid outages, reduce the need for costly grid infrastructure, and support renewable energy integration. BDC with V2L integration is a promising approach for optimal energy management in EVs and can play a significant role in the future of sustainable transportation and energy systems. The proposed model reached 95.13% charging efficiency, 95.03% energy management, 95.69% power rating, 96.28% voltage support and 87.99% temperature management.

In this chapter, there are very novel techniques in which, by deleting nodes that are either overloaded or underloaded and then reassigning the total load to the collective system's nodes, it is possible to maximise the usage of resources and the amount of time it takes for tasks to be completed. The approaches that are utilised for dynamic load balancing are based on the behaviour of the system as it is being utilised right now, as opposed to the behaviour of the system as it was being utilised in the past. When constructing an algorithm of this kind, the most essential considerations to give attention to are the estimation and comparison of load, the stability and performance of the system, the interaction between nodes, the amount of work that needs to be transmitted, and the choice of nodes.

In modern Nigerian supply chain management, the adjudication of intellectual property rights has gained paramount importance. With the rapid advancement of technology, the integration of AI-powered analytics has emerged as a promising resolution for settling disputes and safeguarding intellectual property rights. This chapter highlights the multifaceted role of AI-driven analytics in the adjudication process, exploring its impact on improving efficiency, precision, and impartiality in resolving disputes related to intellectual property within the Nigerian supply chain. The authors suggest that comprehensive ethical standards, data privacy regulations, and transparency protocols should be established by the government, stakeholders, and law enforcement agencies to mitigate potential biases, ensure data integrity, and ensure adherence to ethical and legal norms in AI technologies.

The GATA family of zinc finger DNA-binding transcription factors plays a crucial role in regulating genes responsible for embryogenesis, cellular differentiation, and normal physiological development. This family comprises six members, designated as GATA1 to GATA6, with GATA4 being of particular significance due to its prominent tissue-specific functions. Mutations in GATA4 have been extensively linked with human congenital diseases affecting the heart, lungs, and reproductive system. It is regulated by post-translational modifications, notably phosphorylation at serine 105 by ERK and p38MAPK kinases. The expression and oncogenic/tumor-suppressive roles of GATA4 in cancer have sparked substantial research interest. GATA4 forms essential regulatory complexes with its family members, GATA5 and GATA6 during early myocardial development, and with transcriptional cofactors such as TBX5 and NKx2-5, underscoring its importance in maintaining cellular and molecular homeostasis. Emerging evidence highlights that dysregulated GATA4 expression contributes not only to developmental abnormalities but also to cancer progression, where it may exhibit oncogenic or tumor-suppressive functions depending on the tissue context. These dual roles highlight GATA4 as a crucial molecular node in pathways governing cell survival, proliferation, and tumorigenesis. Therefore, understanding the multifaceted biological implications of GATA4 is indispensable for identifying novel therapeutic targets. This review aims to comprehensively summarize the functional significance of GATA4 in the pathophysiology of developmental disorders and diverse cancers.