AI in phishing detection: a bibliometric review

Artificial Intelligence in the American Healthcare Industry: Looking Forward to 2030

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Artificial Intelligence (AI) is a transformative field at the intersection of computer science, mathematics, and cognitive psychology.It involves the development of systems capable of performing tasks that typically require human intelligence, such as learning, problem-solving, perception, and decision-making.AI technologies, including machine learning, natural language processing, and robotics, are revolutionizing industries ranging from healthcare to finance, and reshaping the way we live, work, and interact with technology."In the 21st century, Artificial Intelligence (AI) has emerged as a transformative force reshaping various aspects of society and industry.With advancements in computing power, data availability, and algorithmic techniques, AI has made significant strides in enabling machines to perform tasks traditionally requiring human intelligence.One of the key drivers of AI progress in the 21st century has been machine learning, a subfield of AI that focuses on algorithms capable of learning from data and making predictions or decisions.Deep learning, a subset of machine learning that uses artificial neural networks with many layers, has particularly fuelled breakthroughs in areas such as image and speech recognition, natural language processing, and autonomous vehicles.AI applications have become pervasive across industries, revolutionizing sectors such as healthcare, finance, transportation, manufacturing, and entertainment.In healthcare, AI is being used for disease diagnosis, drug discovery, personalized medicine, and medical image analysis.In finance, it's employed for fraud detection, algorithmic trading, and customer service.Autonomous vehicles and smart transportation systems leverage AI for navigation, route optimization, and traffic management.Moreover, AI-driven virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous, enhancing user experience and productivity.AIpowered recommendation systems personalize content delivery on platforms like Netflix, Spotify, and Amazon, improving user engagement and satisfaction.However, alongside its transformative potential, AI raises ethical, social, and economic concerns.Issues such as job displacement due to automation, algorithmic biases, data privacy, and the potential misuse of AI for surveillance or military purposes require careful consideration and regulation.Overall, the 21st century has witnessed AI evolve from a theoretical concept to a practical technology with profound implications for society, promising both opportunities and challenges as it continues to advance. Artificial Intelligence and Human Society:Artificial Intelligence (AI) is deeply intertwined with human society, impacting various aspects of our daily lives, work, and culture.Here's how AI influences human society:1. Workforce Automation: AI technologies automate routine and repetitive tasks across industries, leading to increased efficiency and productivity.However, concerns about job displacement and the need for reskilling and upskilling arise as AI replaces certain human roles.2. Healthcare: AI applications in healthcare facilitate disease diagnosis, personalized treatment plans, drug discovery, and medical imaging analysis.AI-driven predictive analytics also help in identifying health risks and optimizing patient outcomes.3. Education: AI enhances education through personalized learning platforms, intelligent tutoring systems, and adaptive assessments tailored to individual student needs.It also enables the development of educational content and resources based on learner preferences and performance.4. Communication and Interaction: AI-driven virtual assistants, chatbots, and language translation services improve communication and accessibility.Natural language processing enables human-like interactions with AI systems, enhancing user experience and accessibility for people with disabilities.

AI has the potential to transform hospital administration and has recently emerged as a solution for improving operational efficiency, enhancing decision-making processes, and optimizing patient care.This paper discusses a few of the AI applications in a hospital administration such as predictive analytics, machine learning and robotic process automation.The article also looks into some of the challenges and hurdles that hinder the mass use of AI like data privacy, integrating systems, and cost of deployment among others.Through examining case studies and literature, this study emphasises the value that AI has for healthcare organizations with discussion around potentially beneficial applications and evolution.The results highlight the need of overcoming implementing challenges to fully leverage AI's potential in healthcare systems. 1.INTRODUCTIONAs a result of increased patient numbers and resource constraints, the operational costs for maintaining quality healthcare services continue to rise.The modern hospital administrator has to cope with a delicate balance of numerous interrelated activities such as: rostering staff, managing resources, overseeing finances, and coordinating the delivery of services to patients (M.Wang & J. Preininger, 2020).These competing priorities cannot be managed by traditional administrative systems that are still largely paper-based, or at best, digitized to a certain extent, hence, much work still being done manually.The implementation of automation and AI technologies, which include machine learning, predictive analytics, and natural language processing, promise to ease the administrative burden AI algorithms can unlock new facets of work improvement throughout the operational workflows.At a minimum, the available AI technologies can perform a wide array of predictive analytics related to staff patient admission, inventory records, and even clinical decision support systems.By shifting the burden of mundane routine work to the automated systems, healthcare institutions may lower their operational costs of serving patients Varghese, Choi, Tan, & Chao, 2017). 2.LITERATURE REVIEWWith the advancement of technology, its capabilities open new opportunities, particularly in healthcare which heavily relies on accurate information.These complex areas are now able to utilize the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA) booming everywhere in an attempt to further improve decision making processes within hospitals, enhance daily operations, and most importantly, provide better care for patients. Machine Learning and Predictive AnalyticsThrough Machine Learning, it is now possible to analyze historical hospital data, patient records e.g.transcripts, and even develop expectations about patients, resource management, and decision making to yield better outcomes (Liu et al., 2018).Analytics is a prospective avenue for patient movement supervision, and resource management (Varghese et al., 2017).

Identifying artificial intelligence (AI) invention: A novel AI patent dataset

International conference proceedings are a forum for researchers in various parts of the world to communicate their research findings to the scientific community and to the public in general. International conference proceedings published by globally recognized institutions are of unquestionable quality. Every conference must have a title that describes the theme or topic being discussed. This study wants to reveal the topic evolution by extracting conference titles. This study collects conference titles that contain terms related to artificial intelligence (AI) in them. Then the titles that have been collected are extracted to obtain a base noun phrase and filtered to eliminate uninformative terms. Of the 139,495 conference titles, it was obtained 8,133 titles containing topics related to AI. The results of the title extraction are 4,601 terms which are dominated by the exact term ‘artificial intelligence’, followed by the terms ‘machine learning’, ‘applications’, ‘intelligent systems’, ‘semantic’, ‘pattern recognition’ and other important related terms. Based on the glossary, frequency of occurrence, and year of publication, further identification of the evolution of a more specific topic is presented in this study. This study identified the early emergence and evolutionary phases of topics for specific terms such as natural language processing, cloud computing, and cybernetics or cyber security. Using the model proposed in this study, the process of identifying the evolution of specific topic can be extracted to get new insights.

AI (Artificial Intelligence) has been a major factor in changing the financial landscape in recent years.  The term "Smart Finance" describes use of AI tools like RPA (robotic process automation), ML (machine learning), natural language processing (NLP) to improve financial decision-making, automate repetitive tasks, and offer more in-depth understanding of how businesses operate.  Financial institutions can improve customer service, manage risk better, streamline operations, and spur long-term development with this data-driven strategy. This chapter explores how AI is transforming finance industry, analyzing its applications, challenges, and opportunities for business growth. We also give a thorough look at future of AI in financial industry and talk about the approaches for integrating AI into financial systems. Combination of AI and finance in the digital era is changing conventional corporate environments. "Smart Finance" refers to integration of AI technologies—involving machine learning (ML), predictive analytics, and NLP—into financial operations to drive strategic decision-making, improve efficiency, and enhance business growth. This paper explores how AI-powered financial systems are transforming data into actionable insights that enable organizations to operate more intelligently and competitively. AI facilitates real-time financial data analysis, enabling companies to forecast trends, detect anomalies, manage risks, and optimize investments with higher accuracy. From automated bookkeeping and fraud detection to intelligent budgeting and customer behavior analysis, AI tools are becoming critical assets in the financial decision-making process. AI improves operational efficiency and frees up financial experts to concentrate on high-level strategic duties by automating repetitive operations and reducing human error. Additionally, platforms powered by AI are able to spot patterns in large datasets that would be very difficult to find by hand. This capability empowers businesses to personalize financial products, improve credit scoring models, and develop dynamic pricing strategies that respond to market shifts. SMEs, in particular, benefit from AI-driven financial insights that were traditionally accessible only to large corporations with extensive resources. But there are drawbacks to using AI in banking as well, such as algorithmic bias, data privacy issues, as well as requirement for qualified personnel to efficiently operate AI systems.  Notwithstanding these challenges, AI has significant long-term benefits for the financial industry, promoting a more robust, informed, and flexible corporate environment.  The objective of this essay is to present thorough examination of how AI is transforming financial operations and promoting long-term company success.  It illustrates that smart finance is not just a technical development but also a strategic necessity for progressive companies through case studies and new trends.

Objective Machine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models compared to general practitioners (GPs). The aim of this study was to evaluate the diagnostic accuracy of an ML classifier on primary headache diagnoses in PHC, compare its performance to GPs, and examine the most impactful signs and symptoms when making a prediction. Design A retrospective study on diagnostic accuracy, using electronic health records from the database of the Primary Health Care Service of the Capital Area (PHCCA) in Iceland. Setting Fifteen primary health care centers of the PHCCA. Subjects All patients that consulted a physician, from 1 January 2006 to 30 April 2020, and received one of the selected diagnoses. Main outcome measures Sensitivity, Specificity, Positive Predictive Value, Matthews Correlation Coefficient, Receiver Operating Characteristic (ROC) curve, and Area under the ROC curve (AUROC) score for primary headache diagnoses, as well as Shapley Additive Explanations (SHAP) values of the ML classifier. Results The classifier outperformed the GPs on all metrics except specificity. The SHAP values indicate that the classifier uses the same signs and symptoms (features) as a physician would, when distinguishing between headache diagnoses. Conclusion In a retrospective comparison, the diagnostic accuracy of the ML classifier for primary headache diagnoses is superior to GPs. According to SHAP values, the ML classifier relies on the same signs and symptoms as a physician when making a diagnostic prediction. Keypoints Little is known about the diagnostic accuracy of machine learning (ML) in the context of primary health care, despite its considerable potential to aid in clinical work. This novel research sheds light on the diagnostic accuracy of ML in a clinical context, as well as the interpretation of its predictions. If the vast potential of ML is to be utilized in primary health care, its performance, safety, and inner workings need to be understood by clinicians.

This paper analyzes how the Artificial Intelligence (AI) and Machine Learning (ML) are bridging the gap between economic growth in the BRICS countries. BRICS countries are emerging economies that are challenged by increasing income inequality, industrial transformation and the need for infrastructure development. Driven by AI, this study applies data analytics to macroeconomic datasets, tracking down patterns and functional takeaways regarding policy formulation and strategic decision making. The research employs techniques, including predictive modeling, clustering, and natural language processing (NLP), in areas such as trade optimization, resource allocation and labour market analysis. Case examples document successful introduction of AI systems to solve critical economic problems, from increasing healthcare access to raising productivity in agriculture. The findings illustrate the role of AI and ML in helping BRICS policymakers to an informed, data driven development. The research puts AI as core to the process of economic advancement, a solution to developmental gaps and a driver for growth. This research contributes both to its practical outcomes and by providing insights into how AI and ML can solve the complex economic problems of emerging markets. The paper introduces predictive modeling, which anticipates economic trends based on past data and clustering which groups similar economic behaviors to find patterns as tools that are important in economic analysis. Further, Natural Language Processing (NLP) is covered as a highly effective approach to understand policy documents, news, and unstructured data to improve the ability to make decisions. By helping students, researchers, and policymakers understand these AI powered techniques that optimize trade, resource management and labor, these scalable solutions to sustainable development are available. This study touts data driven innovation as a critical means to solve global challenges, well-equipped readers with the skills and knowledge to leverage AI for economic progress in a geography of the dynamic and connected.

This paper presents a comprehensive review of Artificial Intelligence (AI) and Machine Learning (ML), exploring foundational concepts, emerging trends, and diverse applications. AI and ML have rapidly evolved, becoming pivotal in numerous fields including healthcare, finance, manufacturing, and autonomous systems. The review begins by outlining key concepts, including the distinctions between AI, ML, and deep learning, and delves into various learning paradigms such as supervised, unsupervised, and reinforcement learning. It highlights significant advancements, such as neural networks, natural language processing, and generative models, emphasizing their impact on industry and research. The paper also examines current trends, including the rise of ethical AI, explain ability, and the integration of AI with Internet of Things (IoT) and edge computing, which are shaping the future landscape of AI applications. Additionally, it addresses the challenges and limitations associated with AI and ML, such as data privacy concerns, model interpretability, and the need for sustainable computing solutions. By synthesizing insights from recent literature, this review provides a holistic understanding of the AI and ML domains, offering perspectives on future directions and innovations. this review aims to provide a holistic understanding of AI and ML, offering perspectives on future directions and innovations.

“The rapid advancement of Artificial Intelligence (AI) has transformed the recruitment landscape, enabling more efficient, data-driven, and adaptive job search mechanisms. Traditional hiring methods often suffer from inefficiencies such as lengthy hiring cycles, unconscious bias, and poor candidate-job matching. AI-driven recruitment systems address these challenges by automating resume screening, analyzing candidate behavior, and providing personalized job recommendations. This paper explores the role of AI in recruitment, focusing on machine learning (ML), natural language processing (NLP), and deep learning techniques used to enhance candidate-job matching. AI-powered tools such as resume parsers, chatbots, and predictive analytics streamline the hiring process, reducing human effort and improving decision-making accuracy. Moreover, adaptive AI algorithms continuously learn from hiring patterns to refine recruitment strategies, increasing efficiency for both employers and job seekers. Despite these advancements, AI-based recruitment presents challenges, including algorithmic bias, ethical concerns, and data privacy issues. This paper discusses potential solutions, such as bias mitigation techniques, explainable AI (XAI), and ethical AI frameworks, to ensure fairness and transparency in hiring. Through case studies of AI-driven recruitment platforms like LinkedIn, HireVue, and IBM Watson, we analyze real-world applications and their impact on job placement success rates. The findings show that AI improves hiring efficiency but requires ethical oversight. The paper concludes by identifying future research directions in AI-powered recruitment, such as blockchain integration and AI-driven psychometric testing, to further improve hiring fairness and effectiveness." Index Terms- Artificial Intelligence, AI recruitment, job matching, machine learning, NLP, automation, bias in hiring, ethical AI.

Artificial intelligence (AI) has emerged as a transformative tool in the diagnosis and management of gastrointestinal (GI) and liver diseases. In clinical practice AI consists of overlapping technologies such as machine learning (ML), deep learning, natural language processing, computer vision, and generative AI. ML is a computer learning system that can provide insight into disease risk factors and phenotypes. Deep learning is an advanced and complex form of ML, structured with different levels of specific algorithms known as convolutional neural networks that can rapidly and accurately process unstructured, high-dimensional data, such as texts, images, and waveforms. Natural language processing is dedicated to facilitating interactions between computers and humans using natural language and helps to analyze, understand, and derive actionable information from unstructured healthcare data, including electronic health records, clinical notes, medical literature, and patient-generated content. Computer vision focuses on enabling computers to see and interpret images and videos and serves as an augmentation tool for endoscopists, improving accuracy and decreasing procedural time. Generative AI is capable of creating new forms of content by learning from a large body of data in the form of text, audio, images, or video and includes large language models. AI has been used in several GI diseases such as esophageal neoplasia, gastric cancer, Helicobacter pylori infection, gastritis, GI stromal tumors, colorectal polyps, inflammatory bowel disease, irritable bowel syndrome, GI bleeding, and pancreatobiliary diseases. The potential applications of AI in liver diseases encompass a variety of conditions such as liver masses, metabolic dysfunction-associated steatotic liver disease, viral hepatitis, cirrhosis, and liver transplantation. This review discussed the common terminologies and the current status of AI in gastroenterology and hepatology, exploring its applications and ethical issues.

The advent of Artificial Intelligence (AI) has revolutionized various domains, and Content Management Systems (CMS) are no exception. Traditional CMS workflows, often reliant on manual processes, have struggled to keep up with the growing demands for scalability, speed, and personalization in managing digital content. AI - powered CMS address these challenges by introducing automation, enhancing efficiency, and delivering personalized user experiences. Through the integration of AI technologies such as Natural Language Processing (NLP), Machine Learning (ML), and Computer Vision, modern CMS have evolved into intelligent platforms capable of automating workflows, optimizing content delivery, and driving data - driven decision - making. This paper explores the transformative potential of AI in CMS, focusing on its applications in workflow automation and user experience enhancement. AI enables the automation of repetitive tasks like content tagging, categorization, and scheduling, significantly reducing manual effort and human error. At the same time, it enhances user engagement by offering personalized content recommendations, optimizing search functionalities, and dynamically adapting content to user contexts. These advancements allow businesses to manage content more effectively and cater to diverse audience needs in real time. However, the integration of AI into CMS is not without challenges. Issues such as data privacy concerns, bias in AI models, and the complexity of integrating AI into legacy systems remain significant barriers. This paper addresses these challenges and proposes actionable recommendations, including the development of explainable AI systems, robust data security frameworks, and scalable architectures that accommodate diverse business needs. As businesses increasingly adopt AI - powered CMS, their potential to transform content management is undeniable. These systems not only streamline workflows and improve operational efficiency but also empower organizations to deliver meaningful and personalized experiences to users. By addressing current challenges and advancing AI technologies, the future of content management will be defined by intelligent, adaptive, and user - centric platforms that bridge the gap between technology and human creativity. This comprehensive review highlights the opportunities and challenges of leveraging AI in CMS and underscores the importance of continued research and innovation to fully realize its potential. AI - powered CMS are not merely tools for managing content but catalysts for digital transformation, enabling organizations to thrive in an increasingly dynamic digital landscape.

The modern world faces numerous challenges in the field of cultural heritage protection, such as natural disasters, climate change, wars, neglect, and limited resources. These factors can lead to irreversible losses of cultural sites and monuments. Against the backdrop of these challenges, the use of artificial intelligence (AI) is becoming an increasingly relevant and promising area in the field of cultural heritage preservation. AI provides unique opportunities to effectively address a number of pressing issues that will help ensure the preservation and transmission of cultural heritage to future generations, improving the efficiency and quality of protection measures. Research in machine learning and artificial intelligence is at the forefront of modern science. The application of these methods to the classification of cultural heritage documents is a relevant area that combines advanced technologies with the need to preserve and study historical and cultural information and allows the use of the latest scientific achievements to solve practical problems. The article analyzes a number of the most relevant artificial intelligence methods used today for the preservation of cultural heritage, summarizes their characteristics and areas of application, in particular, such methods as machine and deep learning, computer vision, natural language processing, robotics, big data processing, semantic networks, and 3D modeling. The advantages of using machine learning in the recognition of cultural heritage documents and examples of their successful application are described. The mechanism of document classification using the Random Forest algorithm as one of the most effective model training tools is described in detail, and a detailed sequence of processes for applying the Random Forest algorithm for document classification is presented. A conceptual model of a machine learning information system for document classification is developed and presented, in particular, an activity diagram for such an information system is developed and further directions of research in this area are outlined.

Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023)