eChapters

Climate refers to the long-term pattern of temperature, rainfall, humidity, wind and other weather parameters within a geographical region. Human activities such as industrialization, urbanization, deforestation, land-use change and intensive agriculture have accelerated climate change by increasing greenhouse-gas emissions. Rising temperatures, irregular monsoon rainfall, droughts, floods, cyclones and elevated atmospheric carbon dioxide now affect crop growth, development and productivity. Different crops and developmental stages vary in their tolerance to these stresses. Consequently, farmers are modifying cropping patterns, sowing periods and crop choices. This chapter examines the effects of changing climatic conditions on major agricultural crops and identifies suitable adaptation and mitigation measures.

Computational intelligence is central to the development of proactive, personalized and connected healthcare systems. This chapter examines artificial intelligence, machine learning, expert systems, medical-image analysis, IoT, big-data analytics, cloud computing and blockchain as key enabling technologies. It presents intelligent architectures for diagnosis, monitoring, treatment support and secure health-data management. Privacy, cybersecurity, explainability, bias, fairness, scalability and deployment challenges are discussed in relation to practical healthcare implementation. The chapter proposes a research roadmap involving trustworthy AI, advanced privacy-preserving mechanisms, edge-cloud intelligence, digital twins, multi-omics integration and personalized health models. It also emphasizes accessible computational-intelligence solutions for rural and resource-constrained healthcare environments

Intelligent healthcare is advancing through the integration of multimodal data, trustworthy AI, federated learning, digital twins, IoT devices and real-time decision-support systems. This chapter reviews the evolution of healthcare-data analysis and examines emerging methods for intelligent diagnosis and predictive care. It discusses explainability, privacy-preserving analytics, interoperability, data governance and ethical frameworks necessary for responsible implementation. Wearable technologies and connected sensors support continuous monitoring, while human–AI collaboration improves clinical decision-making. Case studies illustrate practical healthcare applications and implementation strategies. The chapter also considers global-health requirements, particularly in developing countries, and identifies future opportunities for scalable, equitable, secure and clinically effective intelligent healthcare systems.

Healthcare organizations require collaborative learning methods that protect sensitive patient information. This chapter introduces federated learning as a framework for training models across decentralized institutions without transferring raw clinical data. Horizontal, vertical and hybrid federated architectures are examined alongside communication protocols, threat models and security requirements. Privacy-enhancing methods—including differential privacy, secure aggregation, cryptographic techniques and trusted execution environments—are discussed. Applications in medical imaging, electronic health-record analysis, wearable devices and remote patient monitoring demonstrate the framework’s practical value. The chapter also evaluates accuracy, privacy, communication cost, scalability and robustness while addressing data heterogeneity, fairness, explainability, legal compliance and ethical challenges in collaborative healthcare analytics.

Data-centric artificial intelligence has transformed medical imaging, clinical prediction and electronic health-record analysis, but purely data-driven models face limitations involving generalization, bias, privacy and interpretability. This chapter examines the transition toward knowledge-aware and hybrid healthcare systems that combine machine learning with clinical knowledge, ontologies and knowledge graphs. It discusses explainable AI, clinical reasoning and the integration of structured knowledge into intelligent decision support. Applications include acute-care support, precision medicine, genomic oncology and population-health management. Ethical issues, regulatory compliance, fairness and privacy-preserving technologies are also considered. Future directions include quantum computing, large language models, multimodal integration and equity-focused healthcare-system development.

Predictive analytics estimates future health risks and outcomes, while prescriptive analytics recommends appropriate interventions and resource-allocation strategies. This chapter explains how statistical models, machine learning and healthcare data can support disease-risk assessment, prognosis and personalized treatment. Applications involving cardiovascular disease, diabetes, cancer, chronic-disease management and early-warning systems are examined. The integration of clinical, genomic, lifestyle and electronic health-record data is discussed alongside survival analysis, disease-progression modelling and treatment-response prediction. The chapter also addresses bias, fairness, transparency, privacy, regulation and interoperability. Future developments involving real-time analytics, IoT-enabled monitoring and precision medicine illustrate the transition toward proactive, preventive and data-driven healthcare.

Electronic health records contain extensive clinical, demographic, diagnostic and treatment information that can support medical research and decision-making. This chapter examines the characteristics of healthcare big data and the challenges associated with EHR storage, integration, quality and analysis. It presents an analytics pipeline covering data ingestion, secure storage, preprocessing, integration, pattern discovery, visualization and clinical decision support. Machine learning and deep learning methods are discussed in relation to clinical phenotyping, risk stratification, predictive epidemiology and adverse-outcome prediction. The chapter also addresses data privacy, governance, regulatory compliance, explainability, scalability and bias, highlighting the need for secure and interoperable EHR-mining systems that generate clinically useful knowledge.

Symbolic systems provide logical reasoning and explicit knowledge representation, whereas sub-symbolic models offer learning, adaptation and pattern-recognition capabilities. This chapter explains how combining these approaches can create more accurate, adaptive and interpretable intelligent systems. It discusses rule-based systems, logic programming, ontologies, neural networks, fuzzy systems, evolutionary computation and swarm intelligence. Neural-symbolic, neuro-fuzzy, rule-augmented and ontology-aware architectures are examined, along with loose, tight and deep integration strategies. Applications in healthcare, cybersecurity, manufacturing and knowledge-based information systems demonstrate their practical potential. Challenges involving integration complexity, scalability, knowledge engineering, transparency and continuous updating are considered, together with future directions in human-centred hybrid intelligence.

Advanced artificial-intelligence models can achieve high predictive performance, but their black-box nature limits clinical trust and accountability. This chapter examines interpretability and explainability as essential requirements for trustworthy healthcare analytics. It reviews intrinsically interpretable models, post-hoc explanation methods, example-based approaches and network-based explanations. A framework combining data preparation, model selection, ensemble learning, calibration, uncertainty estimation and explanation validation is presented. An explainable sepsis-risk prediction case study demonstrates logistic modelling, ensemble methods, Bayesian uncertainty, SHAP attribution and network analysis. The chapter also addresses causal reasoning, adversarial robustness, temporal and multimodal data, human-centred explainability, ethical responsibilities and regulatory requirements for dependable clinical AI.

Biomedical signals provide valuable information about physiological conditions and support continuous health monitoring. This chapter introduces ECG, EEG, EMG, PPG, respiratory and blood-pressure signals and examines the challenges associated with their acquisition and interpretation. It discusses noise removal, filtering, normalization, resampling and artifact correction before presenting time-domain, frequency-domain, time–frequency, wavelet and nonlinear feature-extraction methods. Feature selection and dimensionality reduction are considered in relation to model accuracy and clinical interpretability. The chapter also explores machine learning, deep learning and hybrid approaches through applications involving arrhythmia detection, seizure prediction and wearable cardiovascular monitoring, highlighting their importance in intelligent and personalized healthcare.

Clinical decision support systems assist healthcare professionals in diagnosis, treatment planning and patient management. This chapter introduces intelligent clinical decision support systems and explains how computational intelligence overcomes the limitations of conventional rule-based approaches. It examines artificial neural networks, fuzzy logic, evolutionary algorithms, swarm intelligence and deep-learning models. A computational architecture integrating clinical data, learning algorithms, fuzzy inference and optimization is presented. Applications in heart-disease diagnosis, diabetes-risk prediction and medical-image analysis illustrate the practical value of these systems. The chapter also addresses interpretability, interoperability, data quality, privacy, legal responsibilities, real-time processing and the challenges involved in deploying intelligent decision-support tools in clinical environments.

Healthcare artificial intelligence depends on the quality, fairness and reliability of the data used for model development. This chapter examines the characteristics of healthcare data and explains how incomplete, inconsistent or unrepresentative datasets can produce inaccurate and discriminatory clinical outcomes. It identifies sampling, demographic, measurement, historical, institutional and annotation biases and discusses strategies for mitigating them at the data, algorithm and evaluation stages. Model robustness across different populations and clinical environments is also considered. Particular attention is given to explainable AI, human oversight, continuous auditing and interdisciplinary collaboration as essential components of trustworthy, equitable and clinically dependable healthcare systems.

This chapter examines the principles, preparation and application of vaccines and biological products. It compares live attenuated, inactivated, toxoid, subunit, recombinant, vector, DNA and mRNA vaccines. Vaccine development phases, immunogenicity, safety, efficacy and post-marketing surveillance are introduced. The chapter discusses adjuvants, including alum, Freund’s adjuvants, liposomes, ISCOMs and delivery systems. Important veterinary vaccines for brucellosis, foot-and-mouth disease, anthrax, Newcastle disease, infectious bronchitis, goat pox, sheep pox and peste des petits ruminants are covered. Questions also address bacterins, toxoids, attenuation, booster requirements and reversion to virulence, providing practical knowledge of immunoprophylaxis in animal-health management.

This chapter deals with the principles and applications of serological tests used to detect antigens and antibodies. It distinguishes primary, secondary and tertiary binding tests and explains antibody titre, affinity, avidity, sensitivity and specificity. Important techniques include precipitation, agglutination, complement fixation, neutralization, immunofluorescence, ELISA, radioimmunoassay, Western blotting and the radioallergosorbent test. The chapter also addresses serum decomplementation and the interpretation of positive and negative reactions. These methods are central to the laboratory diagnosis, surveillance and control of infectious diseases. The objective questions enable readers to compare diagnostic techniques and select appropriate tests according to their principles, applications and analytical performance.

This chapter introduces hybridoma technology and its application in producing monoclonal antibodies. It explains the fusion of antibody-producing B lymphocytes with immortal myeloma cells, selection in HAT medium and the roles of HGPRT and polyethylene glycol. The survival and cloning of hybrid cells and the advantages of monoclonal antibodies are considered. The chapter also discusses tissue and organ transplantation, including autografts, isografts, allografts and xenografts. The immunological mechanisms of acute graft rejection, donor histocompatibility antigens and the involvement of T lymphocytes are addressed. These questions connect laboratory antibody production with transplantation immunology and its diagnostic, therapeutic and research applications.

This chapter examines immune-system failure arising from reactions against self-antigens or inadequate immune responses. It introduces immunological tolerance, central and peripheral tolerance, clonal deletion and clonal anergy. Important autoimmune diseases—including myasthenia gravis, Graves’ disease, systemic lupus erythematosus, multiple sclerosis, rheumatoid arthritis and Sjögren’s syndrome—are considered. The chapter also discusses molecular mimicry and the production of autoantibodies. Primary, secondary and severe combined immunodeficiency disorders are differentiated, with examples such as Chédiak–Higashi syndrome. Immune-privileged sites and immunological paralysis are also covered. These topics explain the mechanisms that normally prevent self-reactivity and the consequences that arise when immune regulation becomes defective.

This chapter explores the four types of hypersensitivity reactions described under the Gell and Coombs classification. Type I immediate hypersensitivity, Type II cytotoxic reactions, Type III immune-complex disease and Type IV delayed hypersensitivity are examined in detail. The roles of IgE, mast cells, basophils, antibodies, complement and T lymphocytes are highlighted. Clinical examples include anaphylaxis, milk allergy, haemolytic disease, transfusion reactions, serum sickness, glomerulonephritis, farmer’s lung and the blue-eye condition associated with canine hepatitis. Diagnostic applications, including the tuberculin and Coombs tests, are also addressed. The chapter explains how exaggerated or inappropriate immune responses can damage tissues and produce disease.

This chapter examines cytokines as essential signalling molecules that regulate immune and inflammatory responses. It introduces interleukins, interferons, chemokines, tumour necrosis factors and colony-stimulating factors. Their sources, target cells and biological actions are considered in relation to immune-cell activation, differentiation, migration and communication. The chapter explains important properties such as pleiotropy, redundancy, synergy and antagonism. It also covers antiviral activity, chemotaxis, inflammation, fever and the regulation of humoral and cell-mediated immunity. Special attention is given to excessive cytokine production, cytokine storms and septic shock. These questions help readers understand how cytokines coordinate protective immunity and contribute to immune-mediated disease.

This chapter introduces the complement system as an important component of innate and adaptive immunity. It explains the classical, alternative and lectin pathways of complement activation and identifies the molecules involved in each pathway. The formation and functions of C3 convertase, C5 convertase and the membrane attack complex are discussed. Questions address complement-mediated cell lysis, inflammation, chemotaxis and opsonization. The roles of C3b, C3a, C4a, C5a, properdin, factor H and other regulatory proteins are also examined. By covering the activation and regulation of complement, the chapter explains how this system enhances antibody activity and helps eliminate invading microorganisms

This chapter explains how B-cell receptors, T-cell receptors and major histocompatibility complex molecules participate in antigen recognition. It differentiates MHC class I and class II pathways and describes the processing of endogenous and exogenous antigens. The roles of dendritic cells, macrophages and B lymphocytes as professional antigen-presenting cells are examined. Topics include MHC restriction, antigenic peptides, proteasomal degradation, ubiquitination and recognition by CD4-positive and CD8-positive T lymphocytes. The chapter also discusses superantigens, opsonization, phagocytosis, T-independent antigens and species-specific MHC systems such as BoLA and the avian B complex. These concepts explain the initiation of adaptive immune responses.