National American University

Cardiovascular disease is one of the leading causes of death worldwide. Many people develop heart problems without any early warning signs. Because of this, the risk analysis is very important in healthcare if it is correct and early. Traditional risk scores are often simple and incorporate a small number of factors. These methods may not be effective with all patients and populations. Machine learning has become a useful tool for cardiovascular risk analysis. It can research massive amounts of data regarding health and discover concealed patterns. Machine learning models can use data from electronic health records, medical images, ECG signals and wearable devices. Such models could be useful in predicting the risk of heart disease more accurately at an earlier stage and help doctors more accurately predict the risk of heart disease.This review paper is dedicated to study the recent work on machine learning methods for cardiovascular risk analysis. It discusses common sources of data, preprocessing techniques, and model types. Traditional machine learning models and deep learning methods are both discussed. The paper also describes how these models are applied in clinical decision support systems to assist doctors in making better decisions. In addition, this review raises important issues such as model validation, explainability, fairness and trust. Many challenges remain, however, including data quality issues as well as lack of real-world testing. Future research directions are also discussed, including explainable AI and privacy-preserving learning. Overall, this review demonstrates that machine learning has the potential to enhance cardiovascular risk analysis and aid in clinical decision-making. However, careful designing and clinical validation is required before widespread adoption.

Precision Livestock Farming (PLF) has evolved dramatically from basic monitoring systems to sophisticated artificial intelligence (AI)-driven decision support systems that enhance livestock management efficiency, sustainability, and animal welfare. This review examines the technological evolution of PLF since 2017, highlighting significant advancements in sensing technologies, computer vision, and AI. Non-invasive technologies, including red-green-blue and depth cameras, 3D imaging systems, and Internet of Thingsenabled platforms, now capture detailed biometric and behavioral data in real time, while AI algorithms enable early disease detection, optimize feeding strategies, and improve reproductive management. Integrating these technologies with mechanistic models has created hybrid intelligent frameworks that address longstanding challenges in precision nutrition modeling. Future PLF development will likely focus on integrating large language models, adopting federated learning approaches to address data privacy concerns, and democratizing technologies for small-scale producers. Despite technological progress, challenges remain regarding data standardization, connectivity in rural environments, high implementation costs, and ethical considerations around increased animal monitoring. By fostering interdisciplinary collaboration among animal scientists, engineers, computer scientists, and social scientists, PLF can continue to drive sustainable and efficient practices in livestock production while ensuring that technologies complement rather than replace traditional husbandry knowledge.

Artificial Intelligence (AI) is emerging fast in the care of the elderly and demented patients; valuable technologies are being developed to track cognitive health decline in the elderly. With the increased average life span of the global citizen, the demand for early detection and differential management of cognitive impairment has risen. Applications like machine learning and NLP, as well as computer vision, are being used to track cognitive ability and diagnose conditions like dementia – early-stage Alzheimer’s included. These AI-based systems use large amount of data like medical records and neuroimages or behavior of a patient, to understand things like changes in cognition that might not be otherwise detected. Also, AI can provide the development of individual programs of assistance for those patients who need it, which can increase the quality of patient’s life and decrease the load on carers. This paper seeks to understand how artificial intelligence may assist in the management of cognitive health and early identification of dementia in elderly people as well as possible care plans applicable to such persons.

The integration of AI and healthcare can act as the foundation for improving the medical business and the diagnosis and treatment process, as well as patient throughputs. Guided by the following objectives: the current review seeks to discuss what can perhaps be considered as the most revolutionary aspect of care: namely, use of the ML. Software such as the radiology algorithms as well as the prediction tools in healthcare are already narrowing down the error margin. This shifting of life through the practice of genetics by the improvement of AI in managing genetics as the world turns to individualized approach of patients. However, there is a list of challenges that arise when applying AI in the healthcare industry: Data protection and Algorithmic or and The question of who or what is responsible for the AI applications. Clinical decision involving the use of AI has ethical and regulatory issues that need to be addressed therein. But AI does hold a massive amount in advancing the clinical results, in reducing the costs and in making the health care system much stronger, proactive, personalized and efficient. As to the future trends for the use of AI in health care; it will be employed in pharmacology and drug development; in surgery through robot control; and patient management through tele monitoring; as well as in precision care and health information analytics and forecasting. New solutions to still pending issues in data protection, data sharing and objectivity will be important in the future of AI in health care. In sum, this paper proposed that AI is an innovative tool in healthcare’s, that has the potential to redefine the possibilities of how patient care can be delivered, and clinical work can be done, provided the steering wheel of ethical and regulatory burdens is pulled well.

AI is capable of solving many problems in the contemporary healthcare systems, and its application is likely to enhance the diagnostic process, choose individual treatment plans, enhance the level of patient satisfaction and enhance the organization of the delivery of healthcare. Techniques of machine learning and natural language processing based on AI have revealed their efficiency for diagnosing the illness, getting the positive results and client’s oriented recommendations concerning treatment of diseases. Nonetheless, the advancement and use of AI technologies in healthcare brings to the table several innuendos: privacy; security; data control and dissemination; responsibility and prejudice. Luckily however, addressing these challenges needs not only good and advisable policies and guidelines but also cooperation among policy makers, ethicists, technology and health care providers /researchers etc. The purpose of this review is to identify the most common ethical concern regarding the application of AI in health care and the results of practical efforts. It escalates how AI could vary from altering diagnosis approaches, enhancing therapies, controlling documents to raising stress on consumer’s details and securing that those leveraging artificial intelligence act morally and brightly. But the process of getting there into the era of benefit realization from this new technology will open more ideas and details into risks such as bias inference in AI models, patient’s consent and agency, and accessibility of AI technologies in developing countries and remote areas. In conclusion it will be necessary to be purposely reasonable, to be conscious and very communicative in order to introduce AI into better and convenient and humane healthcare.

This essay examines the enormous effects of machine learning and artificial intelligence (AI) on healthcare. Through data analysis, AI is transforming disease detection and prediction and improving the precision of diagnoses. By accelerating medication discovery and improving individualized treatment programs, it is revolutionizing both treatment and drug development. AI is promoting customized medicine by using genetic information to customize therapies. Through automation and optimized resource allocation, it is streamlining hospital processes. The importance of ethical considerations is significant; they center on data privacy, bias reduction, and accountability. The study highlights potential avenues for AI development, such as AI-driven drug discovery, predictive and preventative healthcare, advances in genomic medicine, enhanced medical imaging, and more robotics and automation. Predictive analytics, telehealth, AI virtual assistants, and AI in mental healthcare are all expected to grow. These developments have the potential to improve health care, streamline processes, and boost scientific inquiry. To use AI in healthcare in a fair and ethical manner, however, and usher in a future that is more patient-centric, accurate, and accessible internationally, difficulties related to data quality, ethics, regulation, and prejudice must be addressed.

Aim/Purpose. The purpose of this study is to introduce an instrument that contains a set of exercises intended to help doctoral students align the key sections of their dissertation document. The exercises are developed after providing cognitive analysis of the factors that make aligning these key sections challenging to many, and after discussing pedagogical tools that can be used to address these challenges. Background. Writing doctoral dissertations is a formidable endeavor for numerous students. Among the myriad challenges that are faced is the issue of aligning key sections of the dissertation document. Students often struggle with conceptualizing the alignment among different sections of the various chapters of their dissertation. In this study, we introduce here an instrument that includes a set of exercises to help address the challenges of alignment in chapter one, before the issues spiral and addressing them becomes complicated. Completion of Methodology. This paper reviews literature that discusses the underlying challenges that face the writing of doctoral dissertations in general and the alignment of the key sections in particular. It analyzes the cognitive factors that contribute to the challenges and examines the pedagogical tools that can be used to address these challenges. The review of the literature, the analysis of the cognitive, and the examination of pedagogical tools lead to the introduction of an instrument that is designed to help address the challenges of aligning the key sections of doctoral dissertations. Contribution. This paper presents an instrument with a set of exercises that are intended to help students align key sections of their doctoral dissertation document. This alignment step is crucial to the successful completion of dissertation documents and is best tackled early in the writing. Delaying alignment or worse, ignoring alignment altogether, can complicate the issue and lead to numerous extra steps and delays. Our developed instrument here can be used to tackle this issue of alignment from the beginning and throughout the writing and completion status of dissertation documents. Findings. Students are often faced with challenges when aligning the key sections of a doctoral dissertation. They struggle with conceptualizing the alignment process. They often write each section separately, and independently of other sections of a chapter and a dissertation. However, sections of the dissertation document are interrelated, and each section affects the writing of other sections. For the successful completion of the dissertation, the sections need to be aligned, and it would be best if these issues are tackled from the beginning of the writing and throughout the writing of the dissertation. Recommendations for Practitioners. A methodological approach to aligning the sections of a doctoral dissertation is crucial for the resulting treatise to be coherent and present a unified purpose that threads through each chapter consistently. Recommendations for Researchers. We recommend that doctoral students follow the exercises we introduced in the instrument provided in this paper or take other similar approaches. Without such an approach, aligning the key sections of a doctoral dissertation will be challenging, the dissertation writing process will be more complicated, and the time necessary to complete it will lengthen. Impact on Society. The findings of this research will help doctoral mentors/advisors as they guide students in aligning key sections of their doctoral dissertations.

Tamela Kisner is a nurse educator at the University of South Dakota in Rapid City, S.D. The author has disclosed no financial relationships related to this article.

This chapter describes the expansion of postsecondary or tertiary education throughout the world, the motivations for the expansion, and the commonalities and differences in the systems.

ABSTRACTLibrarians have collaborated with academic departments within their institutions for decades now, working with professors and administrators to bring information literacy skills to students. The librarians at National American University decided to extend this collaboration to a non-academic unit, the admissions department of the university. The admissions department had never worked closely with NAU librarians and many admissions advisors did not see the library as relevant to their role in the organization. Librarians worked with administrative stakeholders to create a tool for admissions advisors to use, aimed at increasing retention and academic success, as well as creating better relationships between admissions advisors, new students, and the library. This tool has increased the library's visibility with new students, admissions, and administrative personnel, as well as built deeper relationships that the library can leverage in future projects. This article offers a way for libraries to build mutually beneficial relationships with admissions staff.