AI Fundamentals: Machine Learning and Big Data

Pharmacovigilance, the science of monitoring drug safety, plays a crucial role in identifying and mitigating adverse drug reactions (ADRs). However, underreporting in pharmacovigilance systems—estimated to have a median rate of 94 %—poses a significant threat to patient safety by hindering the detection of safety signals. The need to address these gaps is paramount, especially with the rising complexity of healthcare data. The advent of artificial intelligence (AI) and big data technologies offers promising solutions to overcome the limitations of traditional pharmacovigilance methods.The application of AI and machine learning (ML) technologies, including natural language processing (NLP) and deep learning, has the potential to revolutionize drug safety monitoring by automating the detection of ADRs from diverse data sources, such as electronic health records (EHRs), spontaneous reporting systems, and social media. These tools can process unstructured data and uncover patterns not easily identifiable through conventional approaches. Additionally, AI can enable real-time pharmacovigilance, which is especially critical in an era of increasing polypharmacy and diverse patient populations. AI-driven models are being utilized to detect drug-drug interactions (DDIs), predict ADRs, and enhance the overall efficiency of pharmacovigilance processes.Despite these advancements, several challenges remain. The performance of AI models is heavily dependent on the quality and quantity of data available. Inadequate or poorly curated datasets can lead to inaccurate ADR detection, particularly in resource-limited settings. Moreover, the heterogeneity of data sources necessitates robust AI models capable of integrating various types of data while ensuring accurate and reliable outputs. There is also a pressing need to address the transparency and explainability of AI models, as the opaque decision-making processes of current algorithms often impede their acceptance among pharmacovigilance professionals.Future directions must focus on improving the quality and standardization of datasets, advancing NLP techniques for better interpretation of clinical narratives, and developing explainable AI models. Regulatory frameworks should evolve to support AI deployment in pharmacovigilance, ensuring the establishment of best practices for AI implementation and the creation of large-scale, publicly available training datasets.Additionally, AI models should go beyond correlation-based approaches by integrating causal inference techniques, which will allow for a more accurate understanding of the relationship between drugs and ADRs. Human oversight will still be required to validate AI findings, but ongoing efforts to improve the robustness of AI systems will reduce dependency on manual interventions and scale the use of AI in pharmacovigilance.The integration of AI and big data in pharmacovigilance has the potential to transform drug safety monitoring, addressing many of the challenges posed by increasing data complexity and the need for real-time analysis. As these technologies continue to evolve, they promise to make pharmacovigilance more efficient, accurate, and comprehensive, thereby improving patient safety and rational drug use.

The digital revolution has profoundly reshaped the landscape of global finance, giving rise to an era where speed, security, and intelligence define the success of financial services. Innovations in Digital Finance and Intelligent Technologies: A Deep Dive into AI, Machine Learning, Cloud Computing, and Big Data in Transforming Global Payments and Financial Services aims to illuminate this transformation by exploring disruptive technologies at the core of today’s financial evolution. In recent years, Artificial Intelligence (AI), Machine Learning (ML), Cloud Computing, and Big Data have moved from theoretical concepts to practical tools that power every facet of modern finance—from algorithmic trading and fraud detection to credit scoring and personalized banking. These innovations are not just enhancing operational efficiency; they are enabling real-time decision-making, improving financial inclusion, and reshaping the way individuals, businesses, and institutions engage with money. This book is crafted for a wide spectrum of readers including financial professionals, technologists, researchers, policy-makers, and students. It unpacks the mechanisms behind intelligent financial systems, provides real-world case studies, and discusses emerging trends such as decentralized finance (DeFi), embedded banking, digital identity, and AI-driven risk assessment. Special attention is given to the regulatory, ethical, and cybersecurity challenges that come with this rapid digitalization. What makes this work timely is its holistic view of innovation—not just as a product of technology, but as a driver of systemic change in global economies. From digital wallets and instant cross-border payments to cloud-native banking platforms, the book outlines how intelligent technologies are setting new standards for agility, transparency, and customer experience. As financial ecosystems grow increasingly complex and interconnected, this book serves as a compass guiding stakeholders toward building secure, inclusive, and intelligent financial futures.

This study investigates the influence of drone technology on the quality of Saudi financial reports through the integration of Artificial Intelligence (AI) and big data. The study’s mixed-method approach is based on a bibliometric analysis of previous studies, along with documentary and content analysis. The results show that external auditors benefit from using drones when inspections are integrated with AI and big data technology. Moreover, this integration can reduce costs for audit firms and shorten the duration of audit engagements, resulting in more efficient and effective auditing. Seven clusters were identified, with ‘big data’ being the highest-frequency term. This study does not consider potential cybersecurity threats that could impact data integrity and decrease financial transparency. Furthermore, environmental issues in Saudi Arabia, such as sandstorms, could compromise the effectiveness of drone-based auditing. However, this study contributes to the ESG literature by demonstrating how integrated audit technology transforms traditional sustainability reporting into continuous, AI-enhanced verification processes. These processes improve financial report quality while supporting Saudi Arabia’s Green Initiative and its goal of achieving net-zero carbon emissions by 2060. The adoption of AI and big data technologies in auditing represents a shift toward more automated and intelligent audit practices. These changes provide practical insights for government authorities, such as the Saudi Capital Market Authority (CMA), and may result in higher-quality financial reports and increased investor confidence.

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

This paper discusses a crucial component of contemporary agriculture: using Big Data, Artificial Intelligence (AI), and the Internet of Things (IoT) to monitor and reduce carbon emissions from agriculture. We concentrate on how these cutting-edge technologies augment Climate-Smart Agriculture (CSA) and advance sustainable farming methods. A thorough analysis of the ways in which IoT, Big Data, and AI can be integrated to track carbon footprints and promote more general sustainability goals in agriculture is given in this research. As a significant contribution, we provide a practical, all-encompassing system architecture that integrates real-time data analytics, predictive modeling, and IoT-enabled sensors created especially for carbon footprint assessment. This paper assesses how well these technologies work to enhance emission monitoring, operational effectiveness, and environmental compliance while highlighting their observable advantages through real-world case studies. The rigorous analysis of issues including data interoperability, device energy efficiency, and implementation costs also sheds light on current research gaps. The report also outlines potential future paths, such as blockchain-based carbon credit trading platforms, scalable IoT-based carbon markets, and machine learning (ML) algorithms for precision farming. The goal is to offer helpful guidance on how to apply cutting-edge technology in the agricultural sector to achieve carbon neutrality and environmental sustainability.

In a world of swift digital transformations, the global market is seeing notable structural shifts. This applies not only to novel technologies, but also to how brands engage with their consumers. Artificial intelligence (AI) and big data analytics are at the heart of this change. With these instruments, companies can analyze consumer behavior more profoundly and precisely, considering the cultural and behavioral traits of diverse audiences worldwide. This allows them to hone their marketing strategies and cultivate a strong emotional bond between the consumer and the brand. The article aims to identify and examine the opportunities that artificial intelligence and big data technologies offer to boost the efficacy of brand communication within the international market. The article reviews theoretical approaches to constructing brand communication in the global market, considering digital transformation, specifically concentrating on the concept and function of brand communication. The key models of brand communication are assessed. The potential of employing artificial intelligence to develop and manage brand communications is examined. The role of big data in making communication decisions, encompassing audience segmentation, analysis of consumer behavior, and content adjustment to cultural specificities is identified. Particular attention is given to the integration of AI and Big Data into a brand’s global communication plan. The pros and cons associated with incorporating AI and Big Data into brand communication strategies, including ethical and legal aspects, are outlined. The article suggests constructing brand communication centered on artificial intelligence and big data, and offers suggestions for brands on the effective application of digital technologies in communicating with consumers on a worldwide scale. Via artificial intelligence and data analytics, brands can ensure a consistent customer experience throughout all stages of interaction. This starts with social media and expands all the way to physical points of sale, which in turn helps to boost customer loyalty and satisfaction. Analytics of behavioral, demographic, and contextual data allows for precise segmentation, recognizing consumer needs, and predicting market trends. This produces the conditions for brands to more effectively tailor content to the cultural and regional attributes of their audience.

PurposeThe purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals.Design/methodology/approachThis paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses.FindingsAI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic.Research limitations/implicationsThere is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well.Social implicationsThis paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant.Originality/valueThis paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.

In public health computing, artificial intelligence (AI) and big data analytics together provide a wealth of fresh approaches to handle significant public health issues, enhance patient outcomes, and guide choices. Standard approaches of analysis may fail to provide real-time insights that can be utilised to move fast as the volume of data in healthcare systems all across the globe rises. Together, artificial intelligence (AI) and big data analytics can manage enormous volumes of various kinds of health data, including social aspects of health, public health data, and electronic health records (EHR). This combination allows one to build prediction models able to detect emerging illnesses, see health trends approaching, and identify groups of persons at risk. From vast volumes of data, artificial intelligence systems—including deep learning and machine learning—can identify helpful patterns. This clarifies risk factors, forecasts disease outbreaks, and guides choices on the most efficient use of resources. Moreover, Big Data analytics allows us to examine large-scale effects of activities, thereby enabling individuals in decision-making to do so grounded on strong evidence. By anticipating how each patient will do, thus improving treatments, and so reducing variations in access to and outcomes of healthcare, using AI and Big Data combined may also assist to personalise healthcare. Using AI and Big Data in public health informatics presents some challenges even with these advances. Concerns concerning data security, the requirement of uniform data formats, and the possibility that algorithms may produce biassed choices abound, for instance. Dealing with these problems is very crucial if we are to guarantee fair and ethical use of Big Data and artificial intelligence to enhance public health choices. This article discusses how Big Data analytics and artificial intelligence will transform public health informatics going forward. It lists their advantages and drawbacks and offers ideas for improving the responses on the pitch.

The integration of Artificial Intelligence (AI) into Human Resources (HR) procedures has heralded the dawn of a new age in the workplace. The purpose of this research study is to shed light on how AI-driven innovations are transforming recruiting, talent management, employee engagement, and the broader HR function. It examines AI's possibilities for decreasing prejudice, increasing efficiency, and optimizing HR decision-making, while also addressing the ethical quandaries and concerns that arise from this fundamental transition. The growing importance of AI and machine learning (ML) in altering HR practices cannot be emphasized. The advent of 'Big Data,' combined with the spread of the Internet of Things (IoT), has created a hospitable environment for breakthrough AI solutions. AI is increasingly being used by businesses to automate HR duties. As a result, AI has the ability to reduce the administrative burden on HR professionals by allowing them to make decisions based on solid data patterns rather than mere guesswork. This conversation digs into AI integration in HR, its benefits, implementation issues, and next steps. Adopting AI and machine learning is critical for precise decision-making and effective staff management. It is critical for HR staff to overcome their fears in order to handle these obstacles. This paper aims to provide a complete knowledge of the emerging HR landscape in the age of AI, presenting insights and ideas to steer enterprises toward a future in which human and artificial intelligence coexist together in the workplace.

Artificial Intelligence (AI) and Big Data Analytics (BDA) are increasingly recognized as transformative forces that are reshaping industries, governance, and human society in profound ways. AI, with its foundations in machine learning, deep learning, natural language processing, and predictive modelling, requires vast amounts of data to train algorithms and generate reliable insights. Complementing this, BDA provides the scale, diversity, and velocity of data processing required to uncover hidden patterns, correlations, and trends within complex datasets. Together, AI and BDA form a synergistic paradigm in which Big Data serves as the fuel and AI operates as the engine, driving intelligent, data-driven innovation across multiple domains. Globally, AI-BDA applications are enabling predictive diagnostics, telemedicine platforms, genomic-driven personalized treatments, and advanced medical imaging, thereby enhancing both the accessibility and quality of care. In India, where the healthcare system must serve more than 1.4 billion citizens with limited resources, these technologies can play a pivotal role in addressing systemic challenges such as inadequate rural access, shortages of medical professionals, real-time disease surveillance, and hospital resource optimization. International best practices, including the NHS AI Lab in the United Kingdom, Mayo Clinic initiatives in the United States, and Ping a Good Doctor in China, provide models that India can adapt and scale to its unique demographic and infrastructural context. Despite their vast potential, the adoption of AI and BDA faces several barriers. Data privacy and cybersecurity remain critical concerns, particularly with the emergence of new regulations and the sensitive nature of health-related information. Additionally, algorithmic bias and the uneven availability of digital infrastructure could exacerbate existing inequities if not adequately addressed. Looking forward, advancements in Edge AI, Explainable AI (XAI), and quantum-enhanced analytics are expected to extend the scope and reliability of AI-BDA systems. This paper argues that the integration of AI and BDA is not merely a technological advancement but a paradigm shifts in how societies innovate, make decisions, and deliver essential services. Specifically, it highlights the need for a human-centric, ethical, and affordable approach to healthcare innovation in India in order to unlock the full transformative potential of these technologies. Keywords: Artificial Intelligence, Big Data Analytics, Healthcare Innovation, Predictive Diagnostics, Ethical AI, Edge AI.

Artificial Intelligence (AI) has emerged as a transformative force in addressing global sustainability challenges, offering innovative solutions for energy optimization, environmental conservation, waste management, and smart urban development. This systematic review critically examines the role of AI in advancing sustainability initiatives by synthesizing peer-reviewed literature published between 2015 and 2024. The study explores how AI-driven technologies, including machine learning, deep learning, and big data analytics, contribute to improving energy efficiency, optimizing resource utilization, and enhancing decision-making processes in sustainability efforts. Key applications include AI-powered smart grids, predictive analytics for climate change mitigation, intelligent waste management systems, and real-time environmental monitoring. Despite its vast potential, the integration of AI into sustainability efforts presents significant challenges. Ethical concerns such as algorithmic bias, lack of transparency in decision-making, and data privacy issues remain critical obstacles. Additionally, high implementation costs, limited interdisciplinary collaboration, and the need for robust policy frameworks hinder the large-scale adoption of AI-driven sustainability solutions. This review underscores the importance of addressing these challenges through inclusive governance, interdisciplinary partnerships, and ethical AI development. Future research should focus on refining AI algorithms for higher accuracy, improving global data-sharing mechanisms, and developing cost-effective AI solutions that are accessible to developing regions. By leveraging AI responsibly and equitably, policymakers, researchers, and industry leaders can drive meaningful progress toward achieving the United Nations' Sustainable Development Goals (SDGs) and fostering a resilient, sustainable future for both present and future generations. Keywords: Artificial Intelligence, Sustainability, Sustainable Development, Energy Optimization, Smart Cities, Machine Learning, Environmental Conservation, Waste Management, Climate Change, Sustainable Development Goals (SDGs).

n the context of total digitalization of education, problems connected with the implementation of artificial intelligence (AI) technologies, as well as issues related to the role of a person as a carrier of human intelligence (HI) in the educational process, become especially topical for universities. The increasing penetration of AI into many areas of higher education requires a balanced approach, considering all the pros and cons, assessing the risks and consequences for humans as subjects of education. The goal of the article is to compare AI and HI capabilities in the educational process of a university. The authors set and solved the following tasks: 1) to study the theoretical material and practical experience of Russian universities in the aspect of applying AI and HI in higher education; 2) to study the concepts of “artificial intelligence” and “human intelligence”; 3) to identify the advantages and disadvantages of AI and HI for the educational process at a university; 4) to present the comparative description of AI and HI in the educational process of a university; 5) to define the perspectives for the development of AI and HI in higher education. To solve the tasks the methods of analysis of scientific literature, analysis of regulatory documents, study and generalization of advanced pedagogical experience, and comparative analysis are used. The results indicate that the issues of introducing AI technologies into the educational process of universities are topical for the state and society. In theory, there is no consensus on the definition of the AI phenomenon, while HI is interpreted as the sum of a person’s cognitive abilities. The review of practice shows a wide range of applicability of AI technologies in universities: from automating routine tasks to creating an immersive educational environment. The comparative analysis of the capabilities of AI and HI has been carried out according to the following parameters: processing and analysis of big data, adaptive learning, interactivity, feedback, creative thinking and critical thinking, complexity of tasks, ethics and morality, emotional communication, objectivity of assessment. The analysis shows that AI and HI in relation to higher education have advantages and disadvantages. A promising scenario for the future development of the system of higher education under the influence of AI technologies will be the integration of AI and HI, which opens up new opportunities for humans in Education 4.0.

Purpose The purpose of this study is to understand how the integration of artificial intelligence (AI) in advertising and marketing is transforming these fields by improving the efficiency and personalization of campaigns. This study analyses scientific output on the application of artificial intelligence (AI) in marketing communication, identifying the main thematic areas, most prolific authors and institutions, collaboration networks and future trends. Design/methodology/approach A bibliometric analysis was conducted based on 614 articles extracted from the Scopus database and filtered by business, social sciences, and economics areas. Tools such as VOSviewer and Bibliometrix were used to examine scientific output, keyword co-occurrence, thematic evolution and international cooperation networks. Findings Since 2016, scientific output has grown by 34.54% annually, demonstrating strong academic interest. The results reveal a multidisciplinary approach with main topics such as big data, machine learning and the metaverse. Four thematic clusters were identified: (1) AI technologies and data analysis, (2) AI applications in marketing strategies, (3) immersive technologies (AR/VR/metaverse) and (4) ethical and critical dimensions such as disinformation. The United States, China and the United Kingdom lead the way in terms of output, but Latin America is poorly represented, highlighting a gap in regional development and scientific cooperation. Originality/value This study offers a comprehensive overview of the evolution and future prospects of AI in marketing communication. It adds value by identifying gaps in academic research and proposing strategic lines for future research.

Big Data usage and Artificial Intelligence (AI) technology combined offer a potential approach to solving challenging problems. AI-driven solutions provide insightful analysis and creative solutions by utilizing the power of big data analytics. With an emphasis on the mediating role of technological literacy and the moderating effect of resource availability, this study investigates the effects of low-cost techniques, the usage of Big Data, and the integration of Artificial Intelligence (AI) on sustainability in landscape design. The purpose of this study is to look at the intricate connections between these factors and how they affect sustainable landscape design methods and results as a whole. A standardized questionnaire was answered by a sample of 458 landscape experts as part of a quantitative approach. Smart PLS (Partial Least Squares), which incorporates evaluations of measurement models, structural models, and mediation and moderation studies, was utilized for data analysis. The study found that using Big Data, implementing low-cost techniques, and incorporating AI all had very favourable effects on sustainability in landscape design. The efficient use of Big Data and AI was found to be mediated by technological literacy, highlighting the importance of this concept in this context. Additionally, resource availability emerged as a critical moderating factor, influencing the strength of these relationships. This research contributes to the field by offering a holistic understanding of the dynamics within sustainable landscape design, emphasizing the importance of integration of AI and utilization of Big Data. It provides practical insights for landscape professionals, informs policy development, and advances educational curricula about AI and Big Data in landscape architecture. The study's limitations include potential response bias due to self-reported data and the cross-sectional design, which restricts the establishment of causal relationships. Additionally, the study focused on professionals, limiting the generalizability of findings to broader community perspectives.

This study examines the relationship between artificial intelligence also it's checking the integration with other technologies. This research aims to explore the complementary relationship between artificial intelligence (AI) and big data analytics, analyzing how each can benefit from the other to enhance the accuracy of analyses, decision-making, and the extraction of broadly applicable insights. The research relied on a descriptive methodology through a review of previous literature and practical applications in sectors such as healthcare, finance, e-commerce, and smart cities. The current era is witnessing a massive surge in the volume of data generated from various sources, posing a significant challenge in extracting valuable insights from it. This research explores how artificial intelligence technologies, such as deep learning and machine learning, can be integrated with big data analytics systems to address these challenges. This study seeks to improve data processing efficiency, increase the accuracy of predictive models, and enable intelligent, real-time decision-making. Through a systematic analysis of the latest literature, the research highlights the anticipated benefits of this integration and discusses the associated technical, legal, and ethical challenges, such as privacy, transparency, and energy consumption