Machine learning–assisted in situ corrosion monitoring: a review

Healthcare is a living system that generates a significant volume of heterogeneous data. As healthcare systems are pivoting to value-based systems, intelligent and interactive analysis of health data is gaining significance for health system management, especially for resource optimization whilst improving care quality and health outcomes. Health data analytics is being influenced by new concepts and intelligent methods emanating from artificial intelligence and big data. In this article, we contextualize health data and health data analytics in terms of the emerging trends of artificial intelligence and big data. We examine the nature of health data using the big data criterion to understand "how big" is health data. Next, we explain the working of artificial intelligence-based data analytics methods and discuss "what insights" can be derived from a broad spectrum of health data analytics methods to improve health system management, health outcomes, knowledge discovery, and healthcare innovation.

The convergence of Artificial Intelligence (AI), Machine Learning (ML), and Big Data with genetic testing and genomic medicine marks a transformative era in healthcare. This book explores the powerful synergy among these domains and their potential to reshape the way we understand, predict, and treat disease—ushering in a new age of personalized medicine. Genomic medicine, with its promise of tailoring healthcare based on an individual's genetic profile, has made significant strides in recent years. However, the vast and complex nature of genomic data presents both opportunities and challenges. This is where AI and ML come into play—offering advanced algorithms and predictive models capable of processing enormous datasets, identifying patterns, and generating actionable insights that were previously beyond human capability. Big Data technologies further support this integration by enabling the collection, storage, and analysis of genomic, clinical, lifestyle, and environmental information at an unprecedented scale and speed.

Mining operations contribute substantially to global environmental degradation. The sector accounts for an estimated 7–9% of global energy consumption and is responsible for widespread groundwater contamination, with acid mine drainage affecting over 12,000 kilometres of streams globally. As global demand for critical minerals surges—driven by the clean energy transition and rapid technological advancement—the mining industry faces mounting pressure to balance productivity with environmental sustainability. This paper explores how artificial intelligence (AI) and big data analytics are revolutionising environmental stewardship in modern mining operations. Through a comprehensive review of literature, real-world case studies, and industry data, the transformative role of AI-enabled technologies such as Internet of Things (IoT) sensors, satellite imaging, and drone-based mapping in reducing environmental impact was examined. These systems provide real-time monitoring, predictive analytics, and automated responses that help mitigate risks such as water contamination, biodiversity loss, and greenhouse gas emissions. Results indicate that AI-driven environmental management systems can reduce water usage by up to 40%, energy consumption by 20%, and pollution-related incidents by over 90%. Despite challenges including data integration complexity and skill gaps, the convergence of AI with quantum computing and advanced sensor networks presents a promising future for sustainable mining. The integration of AI and big data technologies in the mining industry is ushering in a new era of smart, efficient, and sustainable mining practices. By harnessing the power of these transformative tools, mining companies can enhance their competitiveness, mitigate environmental impact, and ensure the long-term viability of the industry. This study proposes a scalable, standardised framework for AI integration to optimise environmental performance, improve economic viability, and enhance stakeholder engagement in the mining sector.

This paper explores the transformative impact of artificial intelligence (AI), blockchain technology, and big data analytics on the sustainable finance sector. These technologies are driving significant advancements in decision-making, regulatory compliance, socially responsible investing (SRI), transparency, efficiency, risk management, financial inclusion, and the identification of sustainable growth opportunities. AI enhances predictive analysis and automates ESG compliance, fostering informed investment strategies and ensuring adherence to sustainability standards. Blockchain introduces unprecedented transparency and efficiency, particularly through smart contracts and decentralized finance (DeFi), facilitating direct funding of sustainable projects and transparent carbon credit trading. Big data analytics empower financial institutions with predictive risk management models and insights for enhancing financial inclusion and identifying sustainable investment opportunities. Through detailed examination, this study underscores how these technologies collectively support the alignment of financial investments with sustainability goals, contributing to the development of a sustainable global economy. This confluence not only streamlines operational processes and compliance but also opens new avenues for sustainable growth and investment, underpinning the financial sector's role in achieving a more sustainable and inclusive future.

Abstract: This book presents a rigorous, interdisciplinary investigation into the convergence of Artificial Intelligence (AI) and Topological Data Analysis (TDA) as a transformative framework for modeling and interpreting high-dimensional data structures. It addresses a fundamental challenge in modern data science: traditional statistical and machine learning techniques often struggle to preserve the global geometric and topological properties of complex datasets. By leveraging tools from algebraic topology—such as persistent homology, simplicial complexes, and Betti numbers—TDA enables the extraction of robust, multi-scale topological features from noisy, sparse, and nonlinear data. The book introduces a comprehensive framework in which topological descriptors are integrated into AI pipelines through persistence diagrams, barcodes, and vectorized representations. Methodologies include differentiable TDA layers, topological regularization in deep learning, manifold learning via Mapper and Reeb graphs, and Bayesian inference with topological priors. Applications span across domains including neuroscience, genomics, medical imaging, finance, and computer vision. Empirical results and case studies demonstrate how topology-aware AI models enhance robustness, reduce overfitting, and provide semantically meaningful representations of data. The book concludes by identifying open challenges—such as the scalability and differentiability of topological operations—and outlines a roadmap for future developments in topology-native machine learning. Through this synthesis, the work establishes TDA not only as a diagnostic tool but as a foundational principle for next-generation AI systems in high-dimensional data environments. Keywords Topological Data Analysis, Artificial Intelligence, Persistent Homology, High-Dimensional Data, Simplicial Complexes, Betti Numbers, Manifold Learning, Mapper Algorithm, Reeb Graphs, Dimensionality Reduction, Topological Priors, Differentiable TDA, Topological Regularization, Federated Learning, Bayesian Inference, Explainable AI, Algebraic Topology, Complex Systems, Geometric Machine Learning, Shape-Aware AI

Photo by Josh Riemer on Unsplash
 Introduction
 With the rapid advancements in neurotechnological machinery and improved analytical insights from machine learning in neuroscience, the availability of big brain data has increased tremendously. Neurological health research is done using digitized brain data.[1] There must be adequate data governance to secure the privacy of subjects participating in brain research and treatments. If not properly regulated, the research methods could lead to significant breaches of the subject’s autonomy and privacy. This paper will address the necessity for neuroprotection laws, which effectively govern the use of big brain data to ensure respect for patient privacy and autonomy.
 Background
 Artificial intelligence and machine learning can be integrated with neuroscience big brain data to drive research studies. This integrative technology allows patterns of electrical activity in neurons to be studied in detail.[2]Specifically, it uses a robotic system which can reason, plan, and exhibit biologically intelligent behavior. Machine learning is a method of computer programming where the code can adapt its behavior based on big brain data.[3] The big brain data is the collection of large amounts of information for the purpose of deciphering patterns through computer analysis using machine learning.[4] The information that these technologies provide is extensive enough to allow a researcher to read a patient’s mind. AI and machine learning technologies work by finding the underlying structure of brain data, which is then described by patterns known as latent factors, eventually resulting in an understanding of the brain’s temporal dynamics.[5]
 Through these technologies, researchers are able to decipher how the human brain computes its performances and thoughts. However, due to the extensive and complex nature of the data processed through AI and machine learning, researchers may gain access to personal information a patient may not wish to reveal. From a bioethical lens, tensions arise in the realm of patient autonomy. Patients are not able to control the transmission of data from their brains that is analyzed by researchers. Governing brain data through laws may enhance the extent of patient privacy in the case where brain data is being used through AI technologies.[6] A responsible approach to governing brain data would require a sophisticated legal structure.
 Analysis
 Impact on Patient Autonomy and Privacy 
 In research pertaining to big brain data, the consent forms do not fully cover the vast amounts of information that is collected. According to research, personal data has become the most sought out commodity to provide content to corporations and the web-based service industry. Unfortunately, data leaks that release private information frequently occur.[7] The storage of an individual’s data on technologies accessible on the internet during research studies makes it vulnerable to leaks, jeopardizing an individual’s privacy. These data leaks may cause the patient to be identified easily, as the degree of information provided by AI technologies are personalized and may be decoded through brain fingerprinting methods.[8]
 There has been an extensive growth in the development and use of AI. It is efficient in providing information to radiologists who diagnose various diseases including brain cancer and psychiatric disease, and AI assists in the delivery of telemedicine.[9] However, the ethical pitfall of reduced patient autonomy must be addressed by analyzing current AI technologies and creating more options for patient preference in how the data may be used. For instance, facial recognition technology[10] commonly used in health care produces more information than listed in common consent forms, threatening to undermine informed consent. Facial recognition software collects extensive data and may disclose more information than a person would prefer to provide despite being a useful tool for diagnosing medical and genetic conditions.[11] In addition, people may not be aware that their images are being used to generate more clinical data for other purposes. It is difficult to guarantee the data is anonymized. Consent requirements must include informing people about the complexity of the potential uses of the data; software developers should maximize patient privacy.[12] Furthermore, there is a “human element” in the use of AI technologies as medical providers control the use and the extent to which data is captured or accessed through the AI technologies.[13] People must understand the scope of the technology and have clear communication with the physician or health care provider about how the medical information will be used. 
 Existing Laws for Brain Data Governance 
 A strict system of defined legal responsibilities of medical providers will ensure a higher degree of patient privacy and autonomy when AI technologies and data from machine learning are used. Governing specific algorithmic data is crucial in safeguarding a patient’s privacy and developing a gold standard treatment protocol following the procurement of the information.[14] Certain AI technologies provide more data than others, and legal boundaries should be established to ensure strong performance, quality control, and scope for patient privacy and autonomy. For instance, currently AI technologies are being used in the realm of intensive neurological care. However, there is a significant level of patient uncertainty about how much control patients have over the data’s uses.[15] Calibrated legal and ethical standards will allow important brain data to be securely governed and monitored.
 Once brain signals are recorded and processed from one individual, the data may be merged with other data in Brain Computer Interface Technology (BCI).[16] To ensure a right and ability to retrieve personal data or pull it from the collection, specific regulations for varying types of data are needed.[17] The importance of consent and patient privacy must be considered through giving patients a transparent view of how brain data is governed.[18] The legal system must address discriminatory issues and risks to patients whose data is used in studies. Laws like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Protection Act (CCPA) can serve as effective models to protect aggregated data. These laws govern consumer information and ensure the compliance when personal data is collected.[19] California voters recently approved expansion of the CCPA to health data. The Washington Privacy Act, which would have provided rights to access, change, and withdraw personal data, failed to pass. Other states should improve privacy as well,[20] although a federal bill would be preferable. Scientists at the Heidelberg Academy of Sciences argue for data security to be governed in a manner that balances patient privacy and autonomy with the commercial interests of researchers.[21] The balance could be achieved through privacy protections like those in the Washington Privacy Act. Although the Health Insurance Portability and Accountability Act (HIPAA) provides an overall framework to deter the likelihood of dangers to patient protection and privacy, more thorough laws are warranted to combat pervasive data transfer and analysis that technology has brought to the health care industry.[22] Breaches of patient privacy under current HIPAA regulations include releasing patient information to a reporter without their consent and sending HIV data to a patient’s employer without consent.[23] HIPAA does not cover information being shared with outside contractors who do not have an agreement with technology companies to keep patient data confidential. HIPAA regulations also do not always address blatant breaches on patient data confidentiality.[24] Patients must be provided with methods to monitor the data being analyzed to be able to view the extent of private information being generated via AI technologies. In health research, the medical purposes of better diagnosis, earlier detection of diseases, or prevention are ethical justifications for the use of the data if it was collected with permission, the person understood and approved the uses of the data, and the data was deidentified.
 A standard governance framework is required in providing the fairest system of care to patients who allow their brain data to be examined. Informed consent in the neuroscience field could reaffirm the privacy and autonomy of patients by ensuring that they understand the type of information collected. Laws also could protect data after a patient’s death. Malpractice in the scope of brain data could give people a cause of action critical in safeguarding patient’s rights. Data breach lawsuits will become common but generally do not cover deidentified data that becomes part of big data collection. A more synchronized approach to the collection and consent process will encourage an understanding of how big data is used to diagnose and treat patients. Some altruistic people may even be more likely to consent if they know the largescale data collection is helpful to treat and diagnose people. Others should have the ability to opt out of sharing neurological data, especially when there is not certainty surrounding deidentification.[25]
 Conclusion
 Artificial intelligence and machine learning technologies have the potential to aid in the diagnosis and treatment of people globally by extracting and aggregating brain data specific to individuals. However, the secure use of the data is necessary to build trust between care providers and patients, as well as in balancing the bioethical principles of beneficence and patient autonomy. We must ensure the highest quality of care to patients, while protecting their privacy, informed consent, and clinical trust. More sophis

In the ever-evolving landscape of global healthcare, the convergence of Artificial Intelligence (AI), Cloud Infrastructure, and Big Data is reshaping how care is delivered, diseases are detected, and patient outcomes are improved. This book, Revolutionizing Healthcare Systems with Next-Generation Technologies: The Role of Artificial Intelligence, Cloud Infrastructure, and Big Data in Driving Patient-Centric Innovation, seeks to explore how these transformative technologies are enabling a shift from reactive to proactive, from generalized to personalized, and from fragmented to integrated healthcare systems. The vision for this work emerged from the growing realization that traditional models of care are no longer sufficient in addressing the complexities of modern medicine. Healthcare providers today must respond to an expanding population, an explosion of health data, and an increasing demand for more accessible, affordable, and individualized care. AI-driven diagnostics, predictive analytics powered by big data, and scalable cloud platforms have become essential tools in this transformation—making healthcare not only smarter but also more compassionate and responsive to individual needs. This book is designed for a wide audience—from researchers, practitioners, and policymakers to technology innovators and students. It offers a comprehensive view of how next-gen technologies are being deployed across healthcare sectors including clinical decision-making, hospital operations, disease surveillance, remote patient monitoring, and precision medicine. It highlights successful implementations, emerging challenges, and ethical considerations, while emphasizing the need for a human-centered approach in tech-driven care. As we look toward the future, this work serves as both a roadmap and a call to action—urging stakeholders to embrace innovation, build interoperable systems, and ensure that the benefits of these technologies reach every patient, regardless of geography or income. With thoughtful collaboration between technologists, clinicians, and communities, we believe that the next revolution in healthcare is not only possible—it is already underway.

In the present day, we find ourselves immersed in an unprecedented technological revolution, driven by the convergence of artificial intelligence, information and communication technologies (ICT), big data analytics, cloud infrastructure, machine learning, and the Internet of Things (IoT). This transformation fundamentally redefines the interaction with information, services, and decision-making. Transparency emerges as a fundamental principle, demanding authenticity, genuine value, and integrity from brands and companies. We are witnessing a paradigm shift in the relationship between people and technology, from artificial intelligence to the Internet of Things IoT, reshaping the way we live, work, and communicate. Society is undergoing a complete digital transformation, reshaping entire sectors and shaping a new digital economy. Artificial intelligence and ICT are building an interconnected society, altering social dynamics, citizen participation, and the way information is consumed. This rapid introduction of technologies challenges traditional structures, demanding adaptability at both individual and organizational levels. In 2024, ethics in data management becomes a crucial pillar to cultivate trust. As privacy, equity in access to technology, and data security become hot topics, attention focuses on implementing ethical principles such as fairness, confidentiality, clarity, and responsibility. The focus on personalized adaptation, along with data privacy and security, stands out as a priority in 2024. While personalization is sought, transparency in data acquisition and usage becomes imperative for companies. The year is proclaimed as “the year of data,” where how brands use their data will make the difference between thriving and perishing. In terms of general figures and data, the amount of information produced and consumed globally is expected to double by 2025, reaching 97 zettabytes. The strategic relevance of data is highlighted, with 87 % of advertisers considering data their most underutilized asset. The percentage of valuable data with potential for analysis has grown from 22 % to 37 % between 2012 and 2020, consolidating the perception that data is the new gold mine in the digital business realm. The big data market is currently valued at $138,9 billion and is still growing. Industry 4,0 increasingly depends on the adoption of big data and artificial intelligence (AI) technologies, with 48,5 % of organizations currently using data as an engine to drive innovation. In 2024, the need for data and AI to go hand in hand to harness their power is emphasized. However, most organizational structures still face challenges in effectively addressing this paradigm shift. It is anticipated that artificial intelligence and algorithms will play a significant role in determining purchasing choices, political preferences, partner selections, family planning, health management, and medical recommendations soon. Trends for 2024 include the intensification of clean data structure usage, effective implementation of these structures, and an increase in “black box” AI solutions. This refers to AI systems and machine learning models that operate hidden from human understanding, driven by complex mathematical models and high-dimensional datasets. Major technology companies are accelerating their support for AI-driven marketing.

This body of knowledge focuses on the impacts that Covid-19 has had on Artificial Intelligence and Big Data Analytics of global organizations and their supply chains or logistics mechanisms through deep learning approaches (Queiroz et al., 2020). The authors of the current study detail it out to define Artificial Intelligence and Big Data Analytics to help further understand and quantify the impact that the Covid-19 pandemic has had on broken supply chains and small businesses (Naude, 2020; Vaishya et al., 2020). Going into further discussion on how the after-effects changed technologies and data analytics for companies, we also point out that organizations require the support of innovation technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big Data and Machine Learning (Moosavi, Fathollahi-Fard, & Dulebenets, 2022) to fight and anticipate against new infections (Queiroz, et al., 2020). The study concludes that COVID-19 has not only devastated the critical thinking, innovation, and sustainability capabilities of scientific organizations but has also deteriorated the in-root causes of future successes of brilliant and extraordinary minds (Jamshidi et al., 2020).

Coronavirus Diseases (COVID-19) is an infectious disease caused by a newly discovered coronavirus that becomes world pandemic with 200 countries recorded affected, and nearly 1 million people died. Starting from Wuhan in December 2019, within three months, the spread across global with high reproduction rates (R Rates). There is evidence in one case, it spread to more than 100 people and creates his pandemic cluster. As the pandemic contributes to a large volume of data, Artificial Intelligence (AI) and Big Data Analytics (BDA) play a huge role in understanding the pan-demic to help necessary action can be deployed. Researchers and developers are increasingly using artificial intelligence, machine learning, and natural language processing to track and contain coronavirus and gain a more comprehensive understanding of the disease. So far, due to new diseases, there is a limited study to cover how AI and BDA will help in fighting COVID-19. Therefore, we provide a comprehensive analysis of the existing and potential of using AI and BDA to manage the COVID-19 outbreak based on COVID-19 Outbreak Life Cycle phases; detection, spread, management, recovery. We also presented the challenges needed to be overcome for BI in BDA in the fighting. To conclude, these findings show the necessity of AI and BDA as a critical tool to understand COVID-19 and there a lot of ongoing intensive works have been carried out to cope with COVID-19.KeywordsCOVID-9Artificial intelligenceBig Data AnalyticsMachine learning

The convergence of artificial intelligence (AI) and big data is reshaping contemporary oncology by enabling the integration of multimodal information across imaging, pathology, genomics, and clinical records. From a physician-centered perspective, these technologies can potentially be used to improve diagnostic precision, support individualized treatment planning, enhance longitudinal patient management, and accelerate both clinical and translational research. In this review, we synthesize the core AI methodologies most relevant to oncology-machine learning, deep learning, and large language models-and examine how they interact with established and emerging oncology data platforms. We further highlight practical use cases in clinical workflows and research pipelines, emphasizing opportunities for advancing precision cancer care while also addressing challenges associated with data heterogeneity, model generalizability, privacy protection, and real-world implementation. By underscoring the synergistic value of AI and big data, this review aims to inform the development of clinically meaningful, context-adapted strategies that promote translational innovation in both global and locally resourced healthcare environments.

The convergence of artificial intelligence and startups has become a key area of research, spurring innovation and transforming the entrepreneurial environment. This study aims to provide an in-depth insight into the intellectual structure and evolution of this dynamic field in the Web of Science database from 2015 to 2025 using Biblioshiny (R studio) and VOSviewer. By employing bibliometric techniques, such as performance analysis and science mapping, it reveals a significant increase in academic and practical interest in artificial intelligence (AI)-driven startups, with an annual growth rate of 28.73% and a peak of 106 publications in 2024. The analysis highlights the leading contributions from authors, institutions and countries, with China, the USA and Italy emerging as key research hubs. The work of Warner and Wäger on digital transformation emerges as the most influential, underscoring the strategic renewal within the AI–startup ecosystem. The thematic analysis showcases a transition from early studies on AI implementation in startups to more advanced themes, such as AI-driven business models, digital transformation, big data analytics and entrepreneurial orientation. Emerging themes, including sentiment analysis and open innovation, alongside foundational areas such as big data analytics and competitive advantage, outline critical pathways for advancing research and practical applications in this field.

The integration of Artificial Intelligence (AI) and Big Data Analytics (BDA) in project management has become a critical enabler of efficiency in managing large-scale, complex projects. This research paper delves into how AI-driven big data analytics can revolutionize traditional project management methodologies by introducing dynamic scheduling, real-time risk prediction, and automated task prioritization strategies. These advanced techniques, which leverage machine learning (ML) models and extensive historical project data, enable a shift from reactive to proactive project management, ensuring that risks and resource constraints are identified and addressed before they impact project delivery. By analyzing massive datasets, including historical performance metrics, resource availability, and project timelines, AI-driven systems can forecast delays, assess risk levels dynamically, and adapt schedules in real-time. This proactive approach facilitates better decision-making, optimized resource allocation, and improved project outcomes. The study is anchored on the premise that the sheer volume of data generated in large-scale projects often overwhelms traditional project management systems. By incorporating AI and BDA, project managers can better utilize this data, turning it into actionable insights that inform intelligent decision-making. Machine learning algorithms, particularly those specializing in predictive analytics, are capable of identifying patterns that elude human analysis, allowing for the accurate forecasting of project risks, schedule slippage, and task dependencies. This ability to predict potential issues, such as resource bottlenecks or unforeseen delays, enables project teams to implement mitigative actions in advance, thus reducing the likelihood of project failure. Furthermore, dynamic scheduling is a key focus of this research, as AI-powered models can continuously adjust project timelines based on real-time data. These models consider variables such as resource utilization rates, task dependencies, and evolving project constraints, offering adaptive scheduling mechanisms that evolve throughout the project lifecycle. The automated task prioritization system, powered by BDA, ensures that the most critical tasks receive the appropriate level of attention at the right time, improving project performance and enhancing resource efficiency. Through natural language processing (NLP) and advanced data mining techniques, AI models can also analyze project documentation and communication channels to detect potential risks and suggest task adjustments. The paper also discusses the application of AI in risk prediction, focusing on how AI models can analyze risk factors from historical data, including resource constraints, financial limitations, and market volatility, to produce risk profiles that project managers can use for strategic planning. Real-time risk assessments, made possible by the integration of AI and BDA, can help project teams stay ahead of potential disruptions. This allows for more accurate contingency planning and reduces the overall risk to project timelines and budgets. Practical applications of these AI-driven strategies are presented through case studies of large-scale projects in various industries, including construction, information technology, and healthcare. These case studies demonstrate how AI-powered analytics have been successfully implemented to enhance project efficiency, optimize resource allocation, and minimize risks in complex projects. The study underscores the importance of integrating these technologies into modern project management frameworks to cope with the increasing complexity of projects in today’s fast-paced business environment. While the potential benefits of AI and BDA in project management are substantial, this paper also addresses the challenges associated with their implementation. One significant challenge is the quality and availability of data required to train AI models effectively. Incomplete or inaccurate data can lead to unreliable forecasts, compromising the project’s success. Additionally, the paper explores the issues of data privacy and security in AI-driven project management systems, highlighting the need for robust data governance frameworks to ensure the ethical use of AI technologies. Another key consideration is the resistance to change within organizations, where traditional project management methods are deeply ingrained. The paper emphasizes the need for a cultural shift towards data-driven decision-making and suggests strategies for fostering an environment conducive to AI adoption. This includes training project management teams to work alongside AI systems and fostering collaboration between AI experts and project managers to ensure smooth implementation and operation. Finally, this research outlines future trends in AI and BDA for project management, suggesting that further advancements in AI technologies, such as reinforcement learning and more sophisticated natural language processing algorithms, will drive the next generation of intelligent project management systems. These future systems are expected to be even more adept at handling the complexities of large-scale projects, offering real-time solutions to unforeseen challenges and adapting dynamically to changing project requirements.

Floods are among the most destructive natural hazards that affect millions of people across the world leading to severe loss of life and damage to properties, critical infrastructure, and the environment. The combination of artificial intelligence (AI), big data, and the Internet of Things (IoTs), has the potential to more accurately predict these extreme events and accelerate the convergence of advanced techniques for flood analytics research. This convergence—so called the Artificial Intelligence of Things (AIoT)—is transformational for both technologies and science-based decision making since AI adds value to IoT through interpretable machine learning (ML) while IoT leverages the power of AI via connectivity and data intelligence. The aim of this research is to discuss the workflow of a Flood Analytics Information System (FAIS; version 4.00) as an example of AIoT prototype to advance and drive the next generation of flood informatics systems. FAIS integrates crowd intelligence, ML, and natural language processing (NLP) to provide flood warning with the aim of improving flood situational awareness and risk assessments. Various image processing algorithms, i.e., Convolutional Neural Networks (CNNs), were also integrated with the FAIS prototype for image label detection, and floodwater level and inundation areas calculation. The prototype successfully identifies a dynamic set of at-risk locations/communities using the USGS river gauge height readings and geotagged tweets intersected with watershed boundary. The list of prioritized locations can be updated, as the river monitoring system and condition change over time (typically every 15 min). The prototype also performs flood frequency analysis (FFA) by fitting multiple probability distributions to the annual flood peak rates and calculates the uncertainty associated with the model. FAIS was operationally tested (beta-tested) during multiple hurricane driven floods in the US and was recently released as a national-scale flood data analytics pipeline.

The convergence of artificial intelligence (AI) and digital genomics is reshaping the future of sustainable agriculture and biotechnology. This paper, Digital Genomics and AI in Agri-Biotech: Innovations for Climate-Resilient Food Futures, provides a comprehensive conceptual and analytical framework for understanding how computational intelligence, big data, and genomic science can jointly address pressing challenges of food security, climate resilience, and global sustainability. The conceptual analysis traces the evolution of AI-driven models in genomics and agri-biotech, identifying critical intersections where predictive analytics, machine learning, and genomic sequencing enhance crop yield, disease resistance, and environmental adaptability. Methodologically, the paper integrates interdisciplinary insights from computer science, systems biology, and agricultural science, employing both quantitative and qualitative approaches to evaluate emerging innovations such as AI-enabled genomic selection, quantum-optimized bioinformatics, and data-driven environmental management systems. Key findings highlight the transformative role of digital genomics in identifying stress-tolerant crop varieties, accelerating the development of bioengineered solutions, and enhancing resource efficiency across agro-ecosystems. Conceptual contributions extend to redefining sustainability frameworks by positioning AI and genomics as central enablers of climate-smart agriculture. The broader implications underscore potential breakthroughs in sustainable food production, equitable resource distribution, and climate adaptation, while also engaging with ethical, social, and policy challenges. By synthesizing technological advances with sustainability imperatives, this work provides both a critical roadmap and a scholarly foundation for future research, innovation, and practice in agri-biotech. Keywords Artificial Intelligence, Digital Genomics, Sustainable Agriculture, Climate Resilience, Agri-Biotechnology, Predictive Analytics, Machine Learning, Quantum Bioinformatics, Food Security, Genomic Selection, Environmental Sustainability, Precision Agriculture, Data-Driven Innovation