AI-Powered Administrative Tools and Secretarial Job Security: A Phenomenological Study in Lesotho Government Ministries | Chapter 7 | New Ideas Concerning Arts and Social Studies Vol. 6

This pilot phenomenological qualitative study explored how five secretaries from a selected Lesotho government ministry perceived job security amid the emerging, though informal, use of AI-powered administrative tools in their daily work. Guided by an interpretivist stance and informed by the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Technology-Organisation-Environment (TOE) frameworks, the study employed a purposive non-probability sampling and conducted in-depth semi-structured interviews to capture participants’ lived experiences. Data were analysed inductively using thematic analysis. Three dominant themes emerged: (1) fear of role displacement; (2) competency gaps, notably limited digital skills and lack of training; and (3) structural limitations, including inadequate infrastructure, weak communication and unclear policy direction. Participants acknowledged the efficiency improvements associated with AI tools but voiced concerns about future roles; inadequate training, limited organisational support and the absence of clear digital transformation plans. Based on the findings, the study recommended a focused strategy that combines targeted reskilling, structured capacity-building and clear, policy-driven digital integration to enable secure and meaningful adoption of AI tools while preserving the secretaries’ distinct professional roles. As a small pilot, the findings were context-specific, and their transferability to other settings should be assessed rather than assumed; nonetheless, they offer applicable, evidence-based guidance for ministries pursuing inclusive and sustainable digital transformation. The study contributed deep insights into AI tools adoption and job security within African public service environments and identifies priorities for policy, training and change management to inform subsequent large-scale qualitative research.

 

 

Author(s) Details

Marethabile Selloane Florina Hoeane-Makote
Information and Corporate Management, Durban University of Technology, Durban, South Africa.

 

Musawenkosi Ngibe
Information and Corporate Management, Durban University of Technology, Durban, South Africa.

 

Please see the book here :- https://doi.org/10.9734/bpi/nicass/v6/6856

Financial Intelligence: From Personal Wealth to Global Impact | Book Publisher International

 

In an era defined by rapid technological advancement and increasingly complex global financial systems, financial intelligence has transitioned from a specialised skill for accountants to a critical competency for individuals, businesses, and law enforcement agencies. This manuscript provides a comprehensive exploration of financial intelligence as both a mindset and a practical toolkit. It defines financial intelligence as the ability to acquire, analyse, and interpret financial data to make informed decisions while adhering to regulatory frameworks and mitigating risks.

 

The book is structured into five core sections that trace the evolution of financial intelligence from its historical roots to its future applications. It details the four main pillars—financial awareness, planning, analysis, and modelling—and demonstrates their application in personal finance, such as budgeting and retirement planning. Furthermore, it explores the strategic role of financial intelligence in business success through cash flow management and performance metrics. It also highlights the importance of financial intelligence in law enforcement, as well as the overall health of the economy.

 

A significant focus is placed on the "dark side" of global finance, providing law enforcement and security personnel with specialised knowledge to combat financial crimes, including money laundering, fraud, and terrorism financing. Finally, the manuscript examines the transformative impact of emerging technologies—such as Artificial Intelligence, blockchain, and big data—on the future economic landscape, emphasising the ongoing necessity of financial literacy for achieving personal security and global economic stability.

 

Author(s) Details

Benjamin Wanger
Intelligence and Security Studies, Nigeria Police Academy, Wudil-Kano, Nigeria.

 

Please see the book here :- https://doi.org/10.9734/bpi/mono/978-93-47485-74-9

Clinical Integration of Artificial Intelligence in Urology with a Focus on Temporal Deep Neural Networks for Emphysematous Pyelonephritis| Chapter 3 | Newer Frontiers in Urology, Volume III

 

Artificial intelligence (AI) is transforming urology by facilitating quick, data-driven analysis in diagnosis and treatment. This chapter explores core AI concepts and their applications in urological conditions like kidney stones, bladder cancer, prostate cancer, and benign prostatic hyperplasia (BPH). Examples of models (such as convolutional neural networks and support vector machines) used in urology are used to teach general AI approaches (machine learning and deep learning). Automated MRI prostate cancer diagnosis (AUC ~0.96), ureteroscopic stone identification (CNN ~90% accuracy), and bladder tumour segmentation are significant achievements.

 

Additionally, the chapter highlights issues with dataset heterogeneity, sample size, and selection bias while briefly discussing the types of datasets used in AI-driven urology research, including imaging, clinical, and longitudinal data. A case study of an emerging multi-task deep neural network (t-MTDNN) for the prediction of emphysematous pyelonephritis (EPN) is included. Feature descriptions (SHAP) are provided for the t-MTDNN architecture and workflow, and its clinical impact and performance metrics are analysed.

 

In conclusion, the strengths and challenges of AI models are compared (Table 1) and prospective opportunities in AI-assisted urology are described with an emphasis on the therapeutic advantages (improved accuracy, efficiency) and limitations (data requirements, interpretability). Instead of replacing clinical judgement, urologists and healthcare organisations view AI as a clinical decision-support tool that can enhance workflow efficiency, support hospital-level adoption through interdisciplinary collaboration, and augment physician expertise.

 

 

Author(s) Details

Roshan Reddy
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Rajan Ravichandran
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Vivek Meyyappan
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Velmurugan Palaniyandi
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Hariharasudhan Sekar
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

Sriram Krishnamoorthy
Department of Urology and Renal Transplantation, Sri Ramachandra Institute of Higher Education & Research Chennai, India.

 

 

Please see the book here :- https://doi.org/10.9734/bpi/mono/978-93-47485-93-0/CH3

Ethical Artificial Intelligence in Education: A Southern African Input–Process–Output (IPO) Governance Framework |Chapter 9 | Mathematics and Computer Science: Research Updates Vol. 8

 

Artificial Intelligence (AI) holds colossal promise for advancing human development, particularly in education, science, and communication. The ethical issues surrounding the design and use of artificial intelligence (AI) have become more important as it becomes more common in schools, government, and society as a whole. The Input–Process–Output (IPO) Ethics Framework is a complete paradigm for finding and dealing with ethical hazards at every stage of the AI lifecycle in response to these problems. This chapter explores the social and ethical considerations of artificial intelligence (AI) as it integrates into education and society. It examines challenges such as data privacy, algorithmic bias, AI trustworthiness, and human agency. The literature highlights context, human agency, and the importance of diverse stakeholder involvement in AI governance, AI literacy, responsible education, and strategies for ethical assessment and mitigation. A literature review of recent articles and policy documents informs this study, focusing on AI’s evolving role in education. Education for AI focuses on Training AI Experts, preparing the Workforce and Public AI Literacy. AI for Education leverages AI tools to enhance teaching, learning, and administrative processes in educational systems. The research develops an input-process-output (IPO) framework to address ethical concerns at each stage of AI development. The IPO model outlines the ethical implications for the input, process, and output phases. Section one addresses AI’s social implications. This chapter also examines AI educational policy using the United Nations Educational, Scientific and Cultural Organisation’s (UNESCO’s) guidelines as a benchmark for member states. Ethical considerations in AI development and usage were also discussed in this chapter. Finally, this chapter presents the AI IPO Ethical Framework, detailing ethical responsibilities at each stage. The study underscores the role of policymakers, researchers, and higher education institutions in shaping AI’s ethical trajectory. It emphasises responsible AI implementation, ensuring that AI systems are developed and deployed with ethical considerations in mind. The proposed framework serves as a guiding tool for assessing ethical risks and ensuring responsible AI integration in education. By fostering AI literacy and ethical awareness, this study contributes to ongoing discussions on AI ethics, advocating for transparent, fair, and accountable AI practices. It aims to support the ethical advancement of AI in education and governance.

 

 

Author(s) Details

 

Mfanelo Ntsobi

Sci-Bono Discovery Centre, Corner of Miriam Makeba and Helen Joseph (Formerly Newtown, Helen Joseph St), Johannesburg, South Africa.

 

Bongani June Mwale
Sci-Bono Discovery Centre, Corner of Miriam Makeba and Helen Joseph (Formerly Newtown, Helen Joseph St), Johannesburg, South Africa.

 

Kholekile Ntsobi
School of Business, DaVinci Institute for Technology Management, 16 Park Ave, Johannesburg, South Africa.

 

Please see the book here :- https://doi.org/10.9734/bpi/mcsru/v8/6832

 

Capabilities of Engineers to Build Machines with Human-like Intelligence Using Artificial Intelligence (AI) and Machine Learning (ML) | Chapter 03 | Recent Research Advances in Arts and Social Studies Vol. 9

 

The study addresses the ongoing debate surrounding the capabilities of engineers to build machines with human-like intelligence using Artificial Intelligence (AI) and Machine Learning (ML). By highlighting the existing obstacles and proposing an alternative approach based on complexity theory and non-linear adaptive systems, the manuscript offers valuable insights and potential solutions to the challenges faced by engineers in the field of AI and ML research. Additionally, it aims to clarify the confusion and misuse of terminology surrounding AI and ML, contributing to greater clarity and understanding within the scientific community. AI and ML are attracting a lot of scientific and engineering attention nowadays, nothing up to now has been achieved to reach the level of building machines that possess human-like intelligence. However, the engineering community continuously claims that several engineering problems are solved using AI or ML. Here, it is argued that engineers are not able to build intelligent machines, implying that the systems claimed to have AI/ML belong to different engineering domains. The base of the syllogism is the existence of four main obstacles on which extensive elucidation is performed. These are (i) lack of precise definition of AI (and ML), (ii) impossible generation of requirements and verification and validation procedures for designing and fabricating machines with intelligence, (iii) no scientific consensus, (iv) philosophical fundamental issues with AI/ML which impose conceptual and assimilation problems in order not to be able making progress if not deal with them. In addition, an attempt to clear out the developed confusion, misuse and abuse of the phrases “Artificial Intelligence” and “Machine Learning” by scientists and engineers is carried out. The confusion is a result of the previous obstacles scientists and engineers are facing and avoid to face, hence creating and growing a kind of “Lusus Naturae” of this scientific field with socio-political impacts as well.  Furthermore, mathematical, and philosophical approaches are also mentioned that strengthen the argument against AI implementability as part of the whole syllogism. Finally, an alternative approach (not being unique) is suggested and discussed for performing research on AI and ML by the engineers. It is based on complexity theory and non-linear adaptive systems and provides the benefit of eliminating the before mentioned pragmatic and philosophical obstacles that engineers are facing and ignoring, without creating confusion on this scientific endeavor. This approach is based on the emergent properties of complex systems. So instead of trying to make the apple (as a symbol of AI), we build the apple tree which through complexity the apple will be grown (symbolically AI will be emanated).

 

Author(s) Details :-

 

Nikolaos Panagiotopoulos
On Board Computers & Data Handling Systems, European Space Research & Technology Centre (ESTEC), Noordwijk, Netherlands.

 

Please see the book here :- https://doi.org/10.9734/bpi/rraass/v9/322

Advancing Healthcare Delivery through Telemedicine and Remote Patient Monitoring: A Comprehensive Review | Chapter 7 | Medical Science: Updates and Prospects Vol. 2

 

Telemedicine and remote patient monitoring (RPM) solutions represent a paradigm shift in healthcare delivery, harnessing the power of digital technologies to improve patient care, enhance accessibility, and reduce costs. Telemedicine facilitates virtual consultations, digital health platforms, and real-time diagnostics, allowing healthcare professionals to interact with patients across geographic barriers. RPM, on the other hand, enables continuous tracking of patient health metrics such as heart rate, glucose levels, and blood pressure through wearable and connected devices, promoting proactive management of chronic diseases and reducing hospital readmissions. Recent advancements, including artificial intelligence (AI) for predictive analytics, wearable biosensors, and high-speed 5G networks, have expanded the scope and efficiency of telemedicine and RPM. These solutions proved invaluable during the COVID-19 pandemic, ensuring continuity of care while minimising physical contact. However, challenges persist, including concerns around data security, interoperability, regulatory barriers, and disparities in access to technology. This review explores the evolution, benefits, and challenges of telemedicine and RPM, while addressing their potential to transform global healthcare systems. It highlights key innovations, regulatory considerations, and the need for equitable access to bridge healthcare gaps. By integrating these technologies into routine care, the future of telemedicine and RPM holds the promise of improved patient outcomes, cost efficiencies, and greater resilience in healthcare systems worldwide.

 

 

Author(s) Details

Vajrala Leela Lakshmi
Department of Pharmaceutics, Narayana Pharmacy College, Nellore -524004, India.

 

S. Naveen Taj
Department of Pharmaceutics, Sri Padmavati Mahila Visvavidyalayam, Tirupati -517502, India.

 

R. Radha
Department of Pharmaceutical Chemistry & Analysis, Sri Venkateswara college of Pharmacy (Autonomous), Chittoor – 517127, India.

 

M. Krishnaveni
Department of Pharmaceutics, Narayana Pharmacy College, Nellore -524004, India.

 

Sibbala Subramanyam
Department of Pharmaceutical Chemistry, Vignan Foundation for Science Technology and Research, Guntur – 522002, India.

 

D. Jothieswari
Department of Pharmaceutical Chemistry & Analysis, Sri Venkateswara college of Pharmacy (Autonomous), Chittoor – 517127, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/msup/v2/6727

Personalised Medicine: From Genomics to 3D-Printed Pharmaceuticals | Chapter 1 | Medical Science: Updates and Prospects Vol. 1

 

Personalised medicine (PM) is a patient-specific approach to treatment that integrates genetic, epigenomic, and clinical data. PM has the potential to transform traditional medical practice by tailoring therapies to individual genetic profiles. The significant advantages and limitations must be carefully considered. Manufacturers are using drug repurposing, biomarker-driven R&D, and collaborations with diagnostics and IT sectors. Personalised medicine not only enhances therapeutic precision but also advances preventive care through polygenic risk scores and early biomarker detection. The integration of digital health tools, including wearables and telemedicine, further supports patient-specific monitoring. Real-world examples, such as FDA-approved targeted therapies and CAR-T cells, illustrate its transformative potential. Innovations such as CRISPR-based interventions, AI-driven decision support, and personalised vaccines are highlighted. Liquid biopsy, single-cell omics, artificial intelligence, and healthcare digitalisation, further supporting its implementation, are cutting-edge tools. The Quality by Design (QbD) principles for safe, consistent, and effective production of personalised 3D-printed tablets have been explained. Critical material attributes (CMAs), critical process parameters (CPPs), and critical quality attributes (CQAs) together enable regulatory-compliant manufacturing by ensuring drug dosage accuracy, content uniformity, dissolution control, and robust production conditions. Beyond treatment, it raises ethical considerations related to data privacy and equitable access. Although cost-intensive, it reduces long-term healthcare burdens by minimising adverse reactions.

 

 

Author(s) Details

B. Navya Sree
Arya College of Pharmacy, India.

 

Saif Bin Salim
Arya College of Pharmacy, India.

 

Mohd Abdul Kareem
Arya College of Pharmacy, India.

 

M. Srikanth
Arya College of Pharmacy, India.

 

 

AVS Rajeswari
Department of Pharmaceutics, Arya College of Pharmacy, India.

 

Please see the book here :- https://doi.org/10.9734/bpi/msup/v1/6352

 

Artificial Intelligence and Virtual Reality in Surgical Training: Enhancing Skill Acquisition and Procedural Proficiency | Chapter 12 | Medical Science: Recent Advances and Applications Vol. 9

 

The integration of Artificial Intelligence (AI) and Virtual Reality (VR) in surgical training has profoundly transformed medical education and technical skill acquisition. In this context, “skill” is defined as a goal-directed motor pattern executed with a specified level of quality and precision, distinct from general abilities, dexterity (purely motor control), and competence (the application of skills in real clinical scenarios). AI-powered simulations, coupled with VR-based training platforms, create highly immersive, interactive, and risk-free environments that allow surgical trainees to practice and refine complex procedures with enhanced precision. These systems simulate real-life anatomical variations and intraoperative scenarios, facilitating improved decision-making and procedural accuracy. Real-time feedback mechanisms, adaptive learning algorithms, and personalised skill enhancement pathways support the development of competence by aligning training with each trainee’s individual learning pace and style. Furthermore, AI enables objective performance tracking through quantitative metrics such as hand movement efficiency, error rates, and task completion times, which support competency-based assessments and predictive modelling of future performance. By overcoming traditional limitations—such as restricted access to cadavers or live patients, variable case exposure, and ethical constraints—AI and VR technologies ensure standardised, reproducible, and scalable surgical training. However, current VR systems remain limited by inadequate haptic feedback, which can restrict the full transfer of tactile skills to real surgical procedures. Examples of current real-world implementations include the da Vinci Surgical System for robot-assisted procedures and augmented 360° VR modules for neurosurgical training. Despite these advances, challenges remain, including high implementation costs, limited haptic feedback, validation requirements for AI algorithms, and ethical considerations such as data privacy and algorithmic bias. By addressing these challenges, AI and VR have the potential to standardise surgical education, improve trainee proficiency, and enhance patient safety. This review comprehensively examines the current advancements, pedagogical benefits, and inherent challenges of incorporating AI and VR into surgical education, emphasising their role in shaping the next generation of surgical professionals and enhancing overall patient safety.

Author(s) Details

 

Ravi Piraji Desai
Department of General Surgery, Banas Medical College and Research Institute, Palanpur, Gujarat, India.

 

Khushboo Patel
GMERS Medical College, Vadnagar, Gujarat, India.

 

Priyanka Paresh Ruparel
Narendra Modi Medical College, Ahmedabad, Gujarat, India.

 

Jaydeep Kagathara
Department of Physiology, Bhagyoday Medical College, Kadi, Gujarat, India.

 

Jitendra Patel
Department of Physiology, GMERS Medical College, Vadnagar, Gujarat, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/msraa/v9/6043

The Role of Artificial Intelligence Techniques in Advancing Agroforestry Systems: A Review | Chapter 11 | New Horizons of Science, Technology and Culture Vol. 4

 

Agriculture plays a crucial role in human survival as a primary source of food, alongside other sources. The introduction of Artificial Intelligence is constantly transforming the present age of agroforestry. It holds significant potential to enhance the sustainability of the agricultural industry through various applications. This review explores about use of artificial intelligence techniques for agroforestry. Agroforestry is an intensive and interactive land usage strategy that maximises biotic and abiotic resources by deliberately combining trees and/or shrubs with crops and/or animals in temporal and spatial patterns on the same plot of land. Agroforestry is a self-sustaining, green and smart technology that will transform the future of Indian agriculture. Artificial intelligence is a powerful technology that encompasses computers and machines to simulate the intelligence of humans to solve specific problems on the basis of logical reasoning and fast experience. AI-powered agroforestry plays a critical role in data collecting, processing, assessment, interpretation, knowledge acquisition, and solution provision to improve overall production and efficiency. It is essential to understand the complexity of the Agroforestry system, cropping patterns, succession, stratification, productivity and biodiversity on the land. However, a larger workforce is required to increase the farm productivity which also enables employment opportunities and smart work in a reconnection with nature. Thus, AI-enabled solutions are extremely valuable in crop cultivation, risk management, crop management, crop protection, crop advice, soil and crop health monitoring and management, crop feeding, automated irrigation, autonomous crop harvesting, crop grading, and even marketing. It will transform contemporary agroforestry methods by enhancing efficiency through accurate real-time monitoring and projections of increased food yields. Thus, the combination of AI, robotics, machine learning, and ancestral knowledge is the path to a transformational technological period that will renew agriculture and agroforestry throughout the world by encompassing varied crops and livestock species. This is also known as Smart Farming, Green Farming, Modern Farming, or Technical Farming. AI systems should be developed and deployed with consideration for local communities, indigenous knowledge, and historically marginalised groups. A sound theoretical framework is a useful basis for guiding the development of specific technical applications of artificial intelligence.

 

 

Author(s) Details

 

Sameer Daniel
Department of Silviculture and Agroforestry, College of Forestry, SHUATS, Prayagraj-211 007 (U.P.), India.

 

Please see the book here:- https://doi.org/10.9734/bpi/nhstc/v4/6182

Evaluation of Machine Learning Model through Stock Price Prediction Research | Chapter 7 | Contemporary Research in Business, Management and Economics Vol. 9

 

This research holds paramount importance in advancing our utilization of artificial intelligence to predict economic factors, notably within the dynamic domain of the stock market. The primary objectives focus on determining the optimal performance among the seven machine learning models employed. Stock investment prices are never still; they are always changing. It is important to stay informed on the upward or downward trends of the market to make future investments. To accustom the machine learning (ML) predictor to the multitude of possibilities that could categorize stock patterns, 7 different ML models were trained on 1250 pieces of open stock market data dating to the last 5 years by assigning weight values to all the models based on their accuracy. The neural network ends up predicting the stock price with its given data at a mediocre level at best, with MSE averages of 29.93 and 26.85 respectively. Its highest weight, tesla, ends up with only 0.013% of the total weightage. Results showed that two of the ML models, specifically the Linear Regression and the Random Sample Consensus (RANSAC) Regressor models consistently outperformed the other 5 models, both ending up with the highest weight values of around 0.5 when predicting for Amazon, Apple, and Tesla. Therefore, the RANSAC and Linear Regression models are the best models to rely on when predicting open stock market prices using ML. Future endeavors must continue this trajectory by expanding model capacities, incorporating richer data sources, and embracing AI-driven advancements to propel stock market predictability into new realms.

 

 

Author(s) Details

Navye Vedant

Inspirit AI, Sammamish, WA, USA.

 

Please see the link:- https://doi.org/10.9734/bpi/crbme/v9/892

 

This article aims to stimulate thoughtful engagement with the philosophical and cultural implications of AI's influence on aesthetics. By examining AI as an embodiment of beauty, this study contributes to the ongoing dialogue on how technological advancements necessitate a revision of established beauty norms. Artificial Intelligence (AI) was considered as an object of beauty through philosophical lenses. The author explores how AI redefines aesthetic norms. The study traces the evolution of beauty from Platonic ideals to contemporary interpretations, arguing that AI's emergence offers a unique illustration of beauty in the modern age. The article delves into the philosophical foundations of beauty, examining historical perspectives and extending this analysis to contemporary theories. Arguments in favor of AI as a manifestation of beauty include its innovative capabilities, the extension of human creativity, and the objective patterns of beauty it can produce. Counterarguments highlight the absence of emotional resonance, the overemphasis on form, and the potential loss of authenticity. The article also compares AI with divine intelligence, discussing parallels and distinctions in terms of intangibility and the human quest for understanding. The research methodology involves a comprehensive literature review, philosophical analysis, and comparative study of classical and modern aesthetic theories. Key sources include works by Plato, Aristotle, Kant, and contemporary scholars in AI and aesthetics. The study area focuses on the intersection of AI, aesthetics, and philosophy, aiming to provide a holistic understanding of AI's potential to be perceived as beautiful. Future research directions include investigating subjective experiences of individuals interacting with AI-generated art, examining cultural influences on the perception of AI's aesthetic value, and developing new metrics for evaluating the beauty of AI systems. Author(s) Details Vadim Meyl Central European University, Vienna, Austria. Please see the link:- https://doi.org/10.9734/bpi/cpassr/v2/944

 

This article aims to stimulate thoughtful engagement with the philosophical and cultural implications of AI's influence on aesthetics. By examining AI as an embodiment of beauty, this study contributes to the ongoing dialogue on how technological advancements necessitate a revision of established beauty norms. Artificial Intelligence (AI) was considered as an object of beauty through philosophical lenses. The author explores how AI redefines aesthetic norms.

 

The study traces the evolution of beauty from Platonic ideals to contemporary interpretations, arguing that AI's emergence offers a unique illustration of beauty in the modern age. The article delves into the philosophical foundations of beauty, examining historical perspectives and extending this analysis to contemporary theories. Arguments in favor of AI as a manifestation of beauty include its innovative capabilities, the extension of human creativity, and the objective patterns of beauty it can produce. Counterarguments highlight the absence of emotional resonance, the overemphasis on form, and the potential loss of authenticity. The article also compares AI with divine intelligence, discussing parallels and distinctions in terms of intangibility and the human quest for understanding.

 

The research methodology involves a comprehensive literature review, philosophical analysis, and comparative study of classical and modern aesthetic theories. Key sources include works by Plato, Aristotle, Kant, and contemporary scholars in AI and aesthetics. The study area focuses on the intersection of AI, aesthetics, and philosophy, aiming to provide a holistic understanding of AI's potential to be perceived as beautiful.

 

Future research directions include investigating subjective experiences of individuals interacting with AI-generated art, examining cultural influences on the perception of AI's aesthetic value, and developing new metrics for evaluating the beauty of AI systems.

 

 

Author(s) Details

Vadim Meyl

Central European University, Vienna, Austria.

 

Please see the link:- https://doi.org/10.9734/bpi/cpassr/v2/944

Artificial Intelligence in Oral Medicine: A Review |Chapter 9 | Medical Research and Its Applications Vol. 10

 

Artificial Intelligence (AI) is increasingly becoming a transformative force in various fields, including oral medicine. A systematic review of AI applications in oral medicine reveals significant advancements and diverse applications that promise to enhance diagnostic accuracy, treatment planning, and patient management. This review synthesizes current research, highlighting the methodologies, outcomes, and potential future directions of AI in oral medicine.

 

Author(s) Details

 

Sameen R. J.

Department of Oral Medicine and Radiology, Faculty of Dentistry, AIMST University, Malaysia.

 

 

Please see the link:- https://doi.org/10.9734/bpi/mria/v10/1137

 

Advances in Systems Analysis of Artificial Intelligence | Chapter 9 | Science and Technology: Recent Updates and Future Prospects Vol. 11

 

Artificial Intelligence (AI) is a branch of computer science dealing with the simulation of intelligent behavior. Artificial Intelligence (AI) constitutes an amorphous phenomenon. There are many different conceptions, subconcepts, and hypotheses offered. Technologies are praised, but the final results remain unclear. It appears to be an industry in its infancy, with the life cycle itself enveloped in a future mystique. This article focuses on methods for enclosing this idea within a systems framework.

 

The article resorts to qualitative analysis. An AI system construct is the ultimate objective of the analysis. It advances a systems theory framework as the boundary for an AI structure where all things would fall into place. The article suggests a framework where an artificial intelligence system has inputs, transformation processes and output. A feedback loop as well. AI Inputs, transformation and output elements are all present-day concepts referred to in current literature but not related to each other within a systems structure.

 

The article concludes with a system construct for artificial intelligence. It further applies this system construct to a contemporary management issue i.e. strategic thinking. It relates system outcomes to strategic thinking patterns and modes. It suggests a strategic thinking framework that makes use of the outcomes of AI system flows, and translates those into strategic behavior patterns and approaches.

 

The article provides a convenient vehicle for understanding the components of artificial intelligence and their flow. Also, the relationship between AI system outcomes and strategic thinking. It could be useful in understanding AI applications and their situational relevance.

 

 

Author(s) Details

M. S. S. El Namaki
School of Management, Victoria University, Switzerland.

 

Please see the link:- https://doi.org/10.9734/bpi/strufp/v11/1671

 

The Rise of FinTech in India: Adoption, Integration, and Future Prospects | Chapter 5 | Science and Technology: Recent Updates and Future Prospects Vol. 11

Technological progress and innovation, which will also support new, disruptive business models in the financial services sector, are the cornerstones of fintech development. "FinTech" (Financial Technology) refers to a broad array of software and innovative technologies that companies use to deliver automated and improved financial services. India, leading in global FinTech adoption with over 6,636 businesses, has one of the world's fastest-growing FinTech markets. The country has achieved significant advancements in digital payments, with over 5.7 billion transactions monthly, totaling around $2 trillion. In 2020, India surpassed the combined real-time online transactions of the US, UK, and China with 25.5 billion transactions. This chapter examines various aspects of the FinTech sector in India, including challenges faced by major companies such as the unbanked population, regulatory hurdles, technology integration, and user experience. It also explores the technologies that underpin FinTech business models, such as blockchain, machine learning, artificial intelligence, cloud computing, and the Internet of Things. By analyzing these elements, this chapter aims to provide a thorough overview of the current state and future potential of the FinTech industry in India. Fintech undergoes a new transformation each year due to the development of technologies and the constantly shifting needs of the financial markets.

 

 

Author(s) Details

Swapnil Sonawane

Vidyalankar Institute of Technology, Mumbai, India.

Dilip Motwani

Vidyalankar Institute of Technology, Mumbai, India.

 

Please see the link:- https://doi.org/10.9734/bpi/strufp/v11/1626

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide and is expected to affect approximately 288 million people globally by 2040. While multimodal imaging has traditionally been the gold standard for diagnosing AMD, optical Coherence Tomography (OCT) provides high-resolution, non-invasive imaging of the retina and has become central to routine disease management. Artificial intelligence (AI) has rapidly emerged as a transformative force across various domains, with its impact particularly notable in ophthalmology and retina imaging, which has opened new avenues for improving diagnostic accuracy, predicting disease progression, and optimizing treatment plans. AI-based algorithms hold great potential for accurately quantifying biomarkers, such as fluid volume and geographic atrophy area in OCT images, predicting disease progression, and assisting in treatment decisions both in clinical practice and academic research. This chapter provides an overview of the current state of AI applications in AMD, highlighting its potential, the challenges encountered, and future prospects in the field. Author(s) Details Virginia Mares Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria and Department of Ophthalmology, Federal University of Minas Gerais, Belo Horizonte, Brazil. Marcio B. Nehemy Department of Ophthalmology, Federal University of Minas Gerais, Belo Horizonte, Brazil. Hrvoje Bogunovic Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria. Sophie Frank Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria. Gregor S. Reiter Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria. Ursula Schmidt-Erfurth Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria. Please see the book here:- https://doi.org/10.9734/bpi/srnta/v6/1942

 

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide and is expected to affect approximately 288 million people globally by 2040. While multimodal imaging has traditionally been the gold standard for diagnosing AMD, optical Coherence Tomography (OCT) provides high-resolution, non-invasive imaging of the retina and has become central to routine disease management. Artificial intelligence (AI) has rapidly emerged as a transformative force across various domains, with its impact particularly notable in ophthalmology and retina imaging, which has opened new avenues for improving diagnostic accuracy, predicting disease progression, and optimizing treatment plans. AI-based algorithms hold great potential for accurately quantifying biomarkers, such as fluid volume and geographic atrophy area in OCT images, predicting disease progression, and assisting in treatment decisions both in clinical practice and academic research. This chapter provides an overview of the current state of AI applications in AMD, highlighting its potential, the challenges encountered, and future prospects in the field.

 

 

Author(s) Details

Virginia Mares

Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria and Department of Ophthalmology, Federal University of Minas Gerais, Belo Horizonte, Brazil.

Marcio B. Nehemy

Department of Ophthalmology, Federal University of Minas Gerais, Belo Horizonte, Brazil.

Hrvoje Bogunovic

Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.

Sophie Frank

Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.

Gregor S. Reiter

Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.

Ursula Schmidt-Erfurth

Laboratory for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria.

 

 

Please see the book here:- https://doi.org/10.9734/bpi/srnta/v6/1942

 

Redefining Medical Education: Balancing Innovation, Expansion and Excellence in the AI Era | Book Publisher International

 

The advent of artificial intelligence (AI) and rapid technological advancements have ushered in a new era in medical education. This monograph, “Redefining Medical Education: Balancing Innovation, Expansion and Excellence in the AI Era,” is a comprehensive academic compilation of 23 chapters that critically examine the evolving paradigms of medical teaching and learning in the 21st century.

 

Bringing together diverse perspectives from specialities such as Paediatrics & Neonatology, General Surgery, Orthopaedics, Radiology, Obstetrics & Gynaecology, and Surgical Superspecialities, the monograph explores how AI and digital tools are transforming medical curricula, clinical training, assessment, research, and faculty development.

 

It highlights key themes such as personalised learning, competency-based education, telemedicine, simulation-based training, ethical use of AI, global collaborations, and equity in medical access and education. The monograph also addresses the pressing need for balancing rapid expansion in medical institutions with sustained quality and academic excellence.

 

Designed for medical educators, postgraduate trainees, policymakers, and healthcare professionals, this work aims to stimulate critical discourse, encourage innovation, and provide strategic insights for shaping the future of medical education in an AI-integrated world—where technological progress harmonises with human values and academic rigour.

 

Author(s) Details

Dr. P. Ramu.
Government Medical College, Srikakulam, Andhra Pradesh, India.

 

Dr. D. Annapurna
Government Medical College, Vizianagaram, Andhra Pradesh, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mono/978-81-991027-5-0

AI in English Higher Education: Balancing Innovation with Equity Challenges and Opportunities | Chapter 4 | An Overview of Literature, Language and Education Research Vol. 3

 

Artificial intelligence, or AI, is revolutionising many industries and our daily lives, workplaces, and educational systems. In the ever-evolving landscape of technology, artificial intelligence (AI) has emerged to challenge traditional paradigms and revolutionise various sectors including higher education. It is now necessary to investigate how AI integration in higher education institutions in England may affect instructional strategies, operational procedures, and student experiences. For example, by utilising AI algorithms, adaptive learning platforms analyse student performance and deliver personalised feedback to increase engagement and information retention. This article explores the implications of AI in the English higher education sector, highlighting the benefits, challenges, and prospects. Moreover, it discusses the potential of AI to improve student experiences, streamline administrative tasks, and transform teaching and learning methods. It emphasizes the benefits of automated assessments, virtual classrooms, and personalised learning. Nevertheless, it is important to consider privacy and ethical concerns, as well as the future role of educators. Therefore, it recognises the necessity of precise regulations and policies to ensure the ethical and responsible application of AI in higher education. By embracing AI while addressing its challenges, England’s higher education institutions can deliver a more inclusive, efficient, and effective learning experience for all. We conclude by providing recommendations for stakeholders, such as focusing on faculty training and data privacy best practices, to navigate the transformative impact of AI on English higher education.

 

Author(s) Details

Sarwar Khawaja
Oxford Business College (OBC), Oxford, UK.

Hengameh Karimi
Oxford Business College (OBC), Oxford, UK.

 

Please see the book here:- https://doi.org/10.9734/bpi/aoller/v3/1147

Navigating the Future of Higher Education: AI Integration and Global Crisis Resilience | Chapter 13 | Crisis, AI and the Future of Higher Education

 

The global higher education landscape has undergone profound disruptions due to crises such as the COVID-19 pandemic, while simultaneously being reshaped by the rapid advancement of artificial intelligence (AI). This chapter synthesizes key findings on the transformative impact of global crises and artificial intelligence (AI) on higher education, employing a literature review method to critically examine research limitations and future directions. Empirical evidence from the literature reveals how the COVID-19 pandemic disrupted graduate education through declining academic engagement and research productivity, with notable gender and disciplinary disparities. Concurrently, AI has reshaped pedagogy through personalized learning and data-driven governance, yet raises ethical concerns regarding algorithmic bias, data privacy, and digital wellbeing. Despite these insights, the research faces limitations in geographic generalizability (primarily China-focused samples), short-term outcome measurement, and insufficient empirical validation of ethical AI frameworks. The chapter proposes three critical future research trajectories: (1) interdisciplinary studies integrating cognitive science and AI ethics to design wellbeing-centric tools, (2) global equity investigations addressing the AI accessibility divide, and (3) policy-research partnerships to develop standardised ethical guidelines. These directions aim to balance technological innovation with equitable, human-centred education systems in an era of disruption. Our study is framed within the theoretical perspectives of crisis resilience and technological disruption, aiming to provide valuable insights for diverse audiences, including researchers, policymakers, and educators.

 

Author(s) Details

Yuanyuan Shi
School of Teacher Education, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.

 

Please see the link:- https://doi.org/10.9734/bpi/mono/978-81-990398-9-6/CH13

The Impact of Artificial Intelligence on College Students’ Learning Strategies, Information Literacy and English Proficiency: A Study in China | Chapter 12 | Crisis, AI and the Future of Higher Education

 

This empirical study examines the transformative impact of Artificial Intelligence (AI) on college students’ English language learning, focusing on the interactions between learning strategies, information literacy and English proficiency. This study collected survey data from 256 college students in Jiangsu Province, China. It used structural equation modelling to examine the direct and indirect effects of learning strategies on English proficiency. The study pinpoints the AI tools frequently utilised by students to facilitate English learning, such as Baidu Translate, Youdao Dictionary, Baicizhan, Shanbay, Bubei, Maimemo, ChatGPT, Liulishuo, Itest, BBC Learning English, Zhixue, Qtyy, Hellotalk, and Callannie, and explores how these tools are incorporated into the students’ learning process. The results of the study show that AI greatly improves students’ English proficiency by improving learning strategies and information literacy. By leveraging AI technologies and optimising learning methods, educational stakeholders can create more effective and inclusive learning environments. This study provides educators and curriculum designers with some insights and practical guidance on how to integrate AI into language learning programs to improve students’ English performance.

 

Author(s) Details

Yuanyuan Shi
School of Teacher Education, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.

 

Please see the link:- https://doi.org/10.9734/bpi/mono/978-81-990398-9-6/CH12

 

Integrating Artificial Intelligence into Learning: An Examination of User Intention, Behavior and Satisfaction | Chapter 11 | Crisis, AI and the Future of Higher Education

 

The rapid advancement of artificial intelligence (AI) has significantly transformed the educational landscape, offering new opportunities to enhance learning experiences. This study explores the relationships among university students’ attitudes toward AI learning tools (AIUI), their usage behaviours (AIUB), and their user satisfaction (AIUS) within the Chinese context. A quantitative research design was employed, involving a cross-sectional survey of 263 valid responses. A convenience sampling method was used to select the study sample. The survey was conducted via a social media platform and an online survey tool. Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) with Smart PLS 4.0 software. The findings reveal that AIUI positively influences both AIUB and AIUS. However, AIUB shows a non-significant direct effect on AIUS, indicating that user satisfaction is more closely related to initial attitudes than usage frequency. These findings underscore the importance of fostering positive attitudes toward AI learning tools and provide valuable insights for educators and developers aiming to improve educational outcomes through AI integration. One significant limitation of this study is the potential for geographical bias, as all participants were from universities in Jiangsu Province, China. Therefore, future research should include a more diverse sample, encompassing students from different regions, educational levels, and cultural backgrounds to gain a more comprehensive understanding of attitudes and behaviours related to AI learning tools.

 

Author(s) Details

Yuanyuan Shi
School of Teacher Education, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.

 

Please see the link:- https://doi.org/10.9734/bpi/mono/978-81-990398-9-6/CH11