Integrating AI and ML Techniques in Modern Microbiology

Background: The field of sports science has been fundamentally transformed by the integration of artificial intelligence (AI) technologies. AI has enabled advances in areas such as performance optimization, training personalization, injury risk assessment and prevention, talent recognition, rehabilitation, athlete monitoring, and wellness optimization. Objective: This review aims to explore the diverse impact of AI in sports science, highlighting advances in AI techniques, the challenges and limitations of integrating AI tools, and the emerging role of AI chatbots in shaping the future of sports research and applications. Methods: The review examines the existing literature on the application of machine learning, deep learning and other AI techniques in various aspects of sports science research and practice. It provides an overview of how these technologies have been used to improve personalized training programs, video analysis, injury risk prediction, talent identification, and rehabilitation. Results: The article describes how AI-powered tools and techniques have revolutionized sports science, enabling personalized, data-driven and efficient approaches to performance optimization. Sophisticated machine learning algorithms such as artificial neural networks, decision trees and support vector machines have been used to develop predictive models for injury risk assessment and prevention, leading to improvements in athletes' wellbeing and long-term performance. AI-driven talent identification and selection processes have also shown promise in recognizing exceptional athletes with greater accuracy. In addition, the integration of AI into athlete monitoring and rehabilitation has led to greater personalization, better decision making and faster return to play. Conclusion: The integration of AI and machine learning techniques into sports science has the potential to transform the field and lead to improved athlete performance, reduced risk of injury, improved well-being, and more personalized and effective interventions. The emergence of AI chatbots expands the applications of these technologies in sports science and offers new opportunities to streamline research processes, provide personalized advice to athletes and support sports medicine and rehabilitation

Diabetes mellitus is a chronic metabolic disorder with significant global prevalence and associated healthcare burdens, necessitating early detection and effective management strategies. The integration of Machine Learning (ML) and Artificial Intelligence (AI) has revolutionized diabetes care, offering innovative approaches to prediction, monitoring, and personalized management. This study conducted a systematic review of 82 high-quality peer-reviewed articles, following the PRISMA guidelines, to provide a comprehensive evaluation of ML and AI applications in diabetes prediction and management. The review highlights the widespread adoption of supervised learning models, such as Random Forest and Support Vector Machines (SVM), which consistently demonstrate high accuracy and reliability in predicting diabetes risk. Ensemble learning methods, particularly Gradient Boosting, emerged as superior techniques for predictive performance, while deep learning models, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), proved effective in analyzing unstructured data such as medical images and time-series glucose data. The integration of AI into wearable devices and mobile health applications has further enhanced real-time monitoring and glycemic control, bridging the gap between technological advancements and practical healthcare solutions. Despite these advancements, challenges such as data imbalance, limited external validation, and the need for explainable AI frameworks persist, underscoring the necessity for methodological rigor and standardization. This review provides critical insights into the current state, limitations, and opportunities of ML and AI in diabetes care, emphasizing their transformative potential in addressing this global health challenge.

Artificial intelligence (AI) is transforming ophthalmology by leveraging machine learning (ML) and deep learning (DL) techniques, particularly artificial neural networks (ANN) and convolutional neural networks (CNN) to mimic human brain functions and enhance accuracy through data exposure. These AI systems are particularly effective in analyzing ophthalmic images for early disease detection, improving diagnostic precision, streamlining clinical workflows, and ultimately enhancing patient outcomes. This study aims to explore the specific applications and impact of AI in the fields of glaucoma, corneal diseases, and oculoplastics. This study reviews current AI technologies in ophthalmology, examining the implementation of ML and DL techniques. It evaluates AI's role in early disease detection, diagnostic accuracy, clinical workflow enhancement, and patient outcomes. AI has significantly advanced the early detection and management of various ocular conditions. In glaucoma, AI systems provide standardized, rapid identification of disease characteristics, reducing intra- and interobserver bias and workload. For corneal diseases, AI tools enhance diagnostic methods for conditions such as keratitis and keratoconus, improving early detection and treatment planning. In oculoplastics, AI assists in the diagnosis and monitoring of eyelid and orbital diseases, facilitating precise surgical planning and postoperative management. The integration of AI in ophthalmology has revolutionized eye care by enhancing diagnostic precision, streamlining clinical workflows, and improving patient outcomes. As AI technologies continue to evolve, their applications in ophthalmology are expected to expand, offering innovative solutions for the diagnosis, monitoring, treatment, and surgical outcomes of various eye conditions.

The integration of artificial intelligence (AI) in education has the potential to revolutionize teaching and learning, particularly in the development of students’ critical thinking skills. This study explores science instructors' familiarity, perceptions, and experiences with using AI to enhance students' critical thinking skills, as well as the level of institutional support for AI integration in teaching. A quantitative survey was conducted among 20 science instructors from higher education institutions in Isabela, Philippines. The findings reveal that while instructors acknowledge AI's potential to improve educational outcomes, there is a significant gap in formal AI training and literacy among educators. Positive correlations were found between AI literacy, AI integration, and critical thinking development, suggesting that as AI literacy increases, AI integration and enhancement of critical thinking skills also increase. Regression analysis identified AI integration as a significant predictor of critical thinking development. Challenges remain in the effective implementation of AI, including concerns about overreliance on AI-generated responses and the need for clear assessment guidelines. Interestingly, years of teaching experience did not significantly influence participants’ AI literacy, perceptions, or integration. This study highlights the importance of developing comprehensive AI literacy programs for educators and integrating AI into curriculum structures to balance AI-enhanced learning with human-centered pedagogy. These findings emphasize the need for thoughtful implementation and ongoing research to effectively leverage AI in promoting critical thinking skills in science education.

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.

The integration of Artificial Intelligence (AI) into financial forecasting has transformed traditional stock market prediction methods. This research paper explores the effectiveness of AI techniques in forecasting stock trends within the Indian stock market over the last few years. We examine AI methodologies, including machine learning (ML), deep learning (DL), and hybrid models applied to the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). By comparing historical data with AI-predicted trends, this study evaluates prediction accuracy and market relevance (Patel et al., 2015). Furthermore, the research outlines existing study gaps and proposes a future scope of integrating AI with behavioral finance and real-time analytics (Chen et al., 2022). The prediction of stock market trends remains a significant challenge due to the stochastic and non-linear nature of financial time series data (Zhang & Zhou, 2020). With the proliferation of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL) techniques, there has been a paradigm shift in the modeling and forecasting of stock price movements (Fischer & Krauss, 2018). This paper presents a comprehensive study on the effectiveness of AI in predicting stock market trends within the Indian financial ecosystem, focusing on a comparative analysis of AI models implemented over the last five years (2018–2023) on major indices and stocks listed on the NSE and BSE. This research evaluates the performance of multiple AI algorithms—including Support Vector Machines (SVM), Random Forests (RF), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRU), and hybrid ensemble models—in forecasting short- and medium-term price trends using historical stock data (Krauss et al., 2017; Chen & He, 2021). Standard evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and classification accuracy are employed to assess model efficacy. The results indicate that while traditional machine learning algorithms offer moderate predictive power, advanced deep learning and hybrid models significantly outperform them, particularly during periods of high market volatility, such as the COVID-19 pandemic and post-lockdown recovery phases (Weng et al., 2018; Jain & Jain, 2021). The study identifies key research gaps, including limited sectoral diversification in datasets, inadequate integration of sentiment and behavioral data, overfitting issues in complex models, and the lack of real-time prediction systems in the Indian context (Gupta & Pathak, 2022). Furthermore, current models often ignore nonquantitative factors such as investor sentiment, macroeconomic indicators, and global events, which can critically impact prediction accuracy (Nassirtoussi et al., 2014). These limitations suggest the need for a more holistic and interdisciplinary approach to AI-driven financial forecasting. The scope for future research includes the development of real-time, adaptive AI systems using high-frequency trading data, the incorporation of behavioral finance through social media and news analytics, and the exploration of quantum computing-based AI models (Liu et al., 2023). From a practical standpoint, the findings of this study offer valuable insights for institutional investors, financial analysts, regulatory bodies, and developers of AIpowered trading platforms. The study concludes that while AI is not a definitive solution for market prediction, it provides a powerful augmentative tool that, when designed with robustness, transparency, and adaptability, can significantly enhance decision-making in India’s fastevolving financial markets.

This book chapter explores the intersection of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), highlighting their transformative impact on various industries and applications. As IoT devices generate massive amounts of real-time data, AI and ML algorithms provide the analytical power necessary to derive actionable insights, automate processes, and enhance decision-making. The chapter covers key AI and ML techniques, such as supervised and unsupervised learning, reinforcement learning, and deep learning, and discusses their integration into IoT systems for improved efficiency, scalability, and real-time performance. Specific use cases, including smart agriculture, industrial IoT, and healthcare, are examined to demonstrate the practical applications of these technologies. Furthermore, the chapter addresses the challenges in data processing, security, and privacy that arise from IoT and AI integration, as well as the future potential of these systems in driving innovation across sectors. This comprehensive analysis serves as a foundational resource for understanding the synergistic relationship between AI, ML, and IoT in creating intelligent, connected systems.

Artificial intelligence (AI) integration into education presents challenging ethical and practical issues as well as transforming possibilities for individualized learning, operational efficiency, and improved outcomes. This report investigates trust, transparency, algorithmic bias, and responsibility in AI-driven educational systems from the points of view of three hundred teachers, educators, and researchers around India. The results highlight a notable knowledge gap: Just 28.33% of respondents are highly familiar with artificial intelligence in education, while 32.67% are completely unfamiliar. Though 35% of respondents are unsure about data privacy policies, 41.67% of respondents believe AI can greatly improve educational outcomes, so they are cautiously optimistic. Only 27.67% of respondents said they were completely aware of how artificial intelligence systems use data. Thus, algorithmic bias became a major issue for 34% of respondents, along with general calls for transparency. The results highlight the need for human supervision since 35.67% of respondents support artificial intelligence use just under such circumstances. This paper comes to the conclusion that tackling these issues calls for strong ethical rules, professional growth to raise AI literacy, and inclusive models for transparency and responsibility. These steps are essential to guarantee fairness and confidence in AI integration, so opening the path for responsible application in learning environments.

Artificial Intelligence (AI) is revolutionizing healthcare by enhancing patient outcomes, reducing costs, and increasing the efficiency of medical professionals. This mini-review explores the diverse applications of AI in healthcare, including disease diagnosis, personalized treatment plans, and patient survival rate predictions. AI technologies such as Machine Learning (ML), deep learning, Natural Language Processing (NLP), and Robotic Process Automation (RPA) are becoming integral to modern healthcare practices. These technologies enable early disease detection, particularly in cases like cancer, by analyzing medical images and patient data, leading to more effective and personalized treatment strategies. Additionally, AI can predict patient outcomes by analyzing large datasets from electronic health records, providing valuable insights that can inform clinical decisions. However, the integration of AI in healthcare also presents significant ethical challenges. Issues such as data privacy, algorithmic bias, lack of transparency, and the potential for increased health inequalities need to be addressed. The World Health Organization (WHO) has provided guidelines emphasizing the ethical use of AI, highlighting the importance of designing AI systems that respect human rights and promote equity. As AI continues to advance, it is crucial to ensure its responsible and transparent use to maximize benefits and minimize risks. This review underscores the transformative potential of AI in healthcare while calling for vigilant ethical considerations to ensure that AI technologies are implemented in a manner that enhances patient care and upholds ethical standards.

The role of AI in transforming psychiatric-mental health care: Enhancing the role of psychiatric-mental health nurse practitioners.

The integration of data science and Artificial Intelligence (AI) into geospatial analysis has revolutionized Earth observation, driving progress towards the Sustainable Development Goals (SDGs). Recent developments in data acquisition technologies like high-resolution satellites and sensors have generated vast and diverse datasets for monitoring environmental changes and managing natural resources. Concurrently, innovations in Machine Learning (ML) and AI have significantly enhanced the processing, analysis and interpretation of this geospatial data. Techniques such as deep learning, spatial data mining and automated feature extraction are now essential to deriving actionable insights from complex geospatial datasets. This paper reviews the latest trends and breakthroughs in the application of AI/ML to geospatial data for Earth observation, emphasizing their role in advancing the SDGs. Key areas of focus include improved algorithms for land cover classification, disaster prediction and climate monitoring. These technologies enable more precise and timely responses to environmental challenges, such as deforestation, urbanization and natural disasters, thereby supporting sustainable management and policymaking. Furthermore, the integration of AI with geospatial data enhances predictive modelling, scenario planning and decision support systems, which are critical for achieving SDG targets related to environmental sustainability and resilience. The synthesis of recent research and technological developments highlights the potential of AI/ML approaches for geospatial analysis and their alignment with global sustainability goals. The outcomes underline the requirement for continued innovation and collaboration across disciplines to fully leverage these advancements for effective Earth observation and sustainable development.

Artificial intelligence (AI) has garnered significant attention in recent years for its potential to revolutionize healthcare, including dentistry. However, despite the growing body of literature on AI-based dental image analysis, challenges such as the integration of AI into clinical workflows, variability in dataset quality, and the lack of standardized evaluation metrics remain largely underexplored. This systematic review aims to address these gaps by assessing the extent to which AI technologies have been integrated into dental specialties, with a specific focus on their applications in dental imaging. A comprehensive review of the literature was conducted, selecting relevant studies through electronic searches from Scopus, Google Scholar, and PubMed databases, covering publications from 2018 to 2023. A total of 52 articles were systematically analyzed to evaluate the diverse approaches of machine learning (ML) and deep learning (DL) in dental imaging. This review reveals that AI has become increasingly prevalent, with researchers predominantly employing convolutional neural networks (CNNs) for detection and diagnosis tasks. Pretrained networks demonstrate strong performance in many scenarios, while ML techniques have shown growing utility in estimation and classification. Key challenges identified include the need for larger, annotated datasets and the translation of research outcomes into clinical practice. The findings underscore AI’s potential to significantly advance diagnostic support, particularly for non-specialist dentists, improving patient care and clinical efficiency. AI-driven software can enhance diagnostic accuracy, facilitate data sharing, and support collaboration among dental professionals. Future developments are anticipated to enable patient-specific optimization of restoration designs and implant placements, leveraging personalized data such as dental history, tissue type, and bone thickness to achieve better outcomes.

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).

This chapter explores the transformative role of Artificial Intelligence (AI) in enhancing Geospatial Intelligence (GEOINT), focusing on how key AI technologies such as machine learning, computer vision, and natural language processing are revolutionizing the analysis of geospatial data. The chapter examines the evolution of GEOINT through AI advancements and delves into practical applications across various domains, including defense, disaster management, environmental monitoring, and urban planning. Future trends, such as the integration of AI with quantum computing, autonomous systems, and augmented reality, are discussed to highlight emerging possibilities. Additionally, the chapter addresses the challenges and ethical considerations associated with AI in GEOINT, including data privacy, algorithmic bias, and data quality issues. This comprehensive analysis aims to provide insights into the future trajectory of AI in geospatial intelligence, unlocking new opportunities for innovation and strategic decision-making.

Introduction: The integration of artificial intelligence (AI) into higher education is swiftly revolutionizing pedagogical methodologies, in conjunction with learning processes and research paradigms. The interdisciplinary potential of AI within academic settings was examined in this study, employing a case study conducted at the University of Tirana. Through the utilization of bibliometric analysis and survey-based research, this study comprehensively investigates the swiftly emerging trends in AI applications, students' significant familiarity with AI technologies, as well as the substantial challenges impeding broader adoption. The bibliometric analysis highlights significant exponential growth in AI research, particularly within pivotal domains such as finance and accounting, thereby emphasizing the swiftly increasing relevance of blockchain and automation. The survey indicates a robust enthusiasm among students for AI in educational settings. More than 90 percent of students actively incorporate AI tools in their project work. Nonetheless, resource limitations and ethical considerations, including privacy, data security, and algorithmic bias, pose considerable challenges to the widespread adoption of AI, despite the prevailing enthusiasm. Objective: The aim of this research is to examine the incorporation of Artificial Intelligence (AI) within the realm of higher education. This analysis concentrates on the applications, advantages, and challenges associated with AI, with the intent of advancing interdisciplinary research and educational methodologies at the University of Tirana. Theoretical Framework: This research expands upon the principles of AI adoption within the educational sector, alongside an examination of ethical considerations and multidisciplinary collaboration. Theories pertaining to technological integration and adaptive learning systems serve as the foundational framework for comprehending the implications of AI in the realm of education. Method: The methodology employs bibliometric analysis to examine AI-related research trends utilizing data from SCOPUS and conducts a survey to assess students' familiarity with and perceptions of AI. Data collection was facilitated through bibliometric instruments and an online survey incorporating Likert-scale and open-ended questions. Results and Discussion: The results underscore an increased focus on artificial intelligence (AI) and blockchain within scholarly research, wherein students exhibit considerable engagement and interest in AI applications. Nevertheless, limitations in resources and ethical issues, including privacy and bias, persist as primary challenges. The discourse underscores the imperative for investment in infrastructure and the incorporation of ethical education. Research Implications: This research highlights the imperative for higher education institutions to integrate artificial intelligence tools, cultivate adaptive curricula, and address ethical considerations in order to adequately prepare students for a future shaped by AI. The implications of these findings also pertain to educational policy and the formulation of interdisciplinary research strategies. Originality/Value: The study contributes by delivering a comprehensive bibliometric analysis and gives insights into student engagement with artificial intelligence. Its significance resides in presenting actionable recommendations to enhance the integration of artificial intelligence in higher education.