Application Of Artificial Intelligence Research Articles

Human Adaption to Climate Change: Marine Disaster Risk Reduction in the Era of Intelligence

With the intensification of global warming and sea level rise, extreme weather and climate events occur frequently, increasing the probability and destructive power of marine disasters. The purpose of this paper is to propose the specific application of artificial intelligence (AI) in marine disaster risk reduction. First, this paper uses computer vision to assess the vulnerability of the target and then uses CNN-LSTM to forecast tropical cyclones. Second, this paper proposes a social media communication mechanism based on deep learning and a psychological crisis intervention mechanism based on AIGC. In addition, the rescue response system based on an intelligent unmanned platform is also the focus of this research. Third, this paper also attempts to discuss disaster loss assessment and reconstruction based on machine learning and smart city concepts. After proposing specific application measures, this paper proposes three policy recommendations. The first one is improving legislation to break the technological trap of AI. The second one is promoting scientific and technological innovation to break through key technologies of AI. The third one is strengthening coordination and cooperation to build a disaster reduction system that integrates man and machine. The purpose of this paper is to reduce the risk of marine disasters by applying AI. Furthermore, we hope to provide scientific references for sustainability and human adaptation to climate change.

AI-Driven Precision Agriculture: Optimizing Crop Yield and Resource Efficiency

This article explores the multifaceted applications of Artificial Intelligence (AI) technologies in precision agriculture, focusing on their potential to significantly enhance crop yields while optimizing resource utilization. The article examines five key areas where AI is making substantial impacts: predictive analytics for crop management, intelligent irrigation systems, automated pest and disease detection, precision fertilizer application, and robotic harvesting. By integrating data from various sources and employing advanced machine learning algorithms, these AI-driven systems demonstrate remarkable improvements in efficiency, accuracy, and sustainability. The article highlights significant advancements, such as a 15% improvement in yield prediction accuracy, up to 30% reduction in water usage, and a 20% decrease in fertilizer use without compromising crop yields. While acknowledging challenges such as data privacy concerns and initial investment costs, the article underscores the long-term benefits of AI adoption in agriculture, including increased profitability, environmental sustainability, and improved food security. This comprehensive analysis provides insights into how AI-driven precision agriculture is reshaping modern farming practices and its potential to address global food production challenges while reducing agriculture's environmental footprint.

Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery

Leveraging its ability to handle large and complex datasets, artificial intelligence can uncover subtle patterns and correlations that human observation may overlook. This is particularly valuable for understanding the intricate dynamics of spinal surgery and its multifaceted impacts on patient prognosis. This review aims to delineate the role of artificial intelligence in spinal surgery. A search of the PubMed database from 1992 to 2023 was conducted using relevant English publications related to the application of artificial intelligence in spinal surgery. The search strategy involved a combination of the following keywords: "Artificial neural network," "deep learning," "artificial intelligence," "spinal," "musculoskeletal," "lumbar," "vertebra," "disc," "cervical," "cord," "stenosis," "procedure," "operation," "surgery," "preoperative," "postoperative," and "operative." A total of 1,182 articles were retrieved. After a careful evaluation of abstracts, 90 articles were found to meet the inclusion criteria for this review. Our review highlights various applications of artificial neural networks in spinal disease management, including (1) assessing surgical indications, (2) assisting in surgical procedures, (3) preoperatively predicting surgical outcomes, and (4) estimating the occurrence of various surgical complications and adverse events. By utilizing these technologies, surgical outcomes can be improved, ultimately enhancing the quality of life for patients.

Artificial intelligence: basic terms and concepts, the application in healthcare and clinical medicine

Objective: to explore the potential and challenges of artificial intelligence (AI) in clinical medicine and healthcare, and to determine the prospects for its implementation to improve diagnosis, treatment, and medical data management.Material and methods. A literature review on the main terms and concepts of AI, its classification by application area, technologies, and methodologies was carried out. The learning methods such as supervised, unsupervised, and reinforcement learning were considered, as well as examples of AI application in various areas of medicine, including disease diagnosis and personalized medicine.Results. AI shows significant potential in improving diagnosis, optimizing treatment processes, and managing healthcare resources. Main application areas are related to medical image analysis, developing individualized treatment plans, and healthcare management. However, using AI faces challenges such as data availability and bias, fragmentation of systems, and complexity of algorithm interpretation.Conclusion. Despite the existing challenges, the implementation of AI in medicine has great prospects, including improved diagnostic accuracy, reduced task completion time, and development of personalized medicine. It is important to consider the ethical aspects and the demand for further study of AI application in medicine to achieve the best results.

Artificial intelligence for internal communication: Strategies, challenges, and implications

This qualitative study explores the intersection of artificial intelligence (AI) and internal communication, investigating AI applications, challenges, and best practices. In-depth interviews with 20 senior communication professionals examined perceptions of AI’s impact on internal communication processes and outcomes. Key findings indicate AI improves internal communication efficiency, information flow, listening capability, and employee experience. However, challenges include lack of AI knowledge among employees, fears about job security andbias, and concerns about authenticity loss. To enable effective AI adoption for internal communication, organizations need education and training, a “co-piloting” approach between humans and machines, transparent communication, ethical governance, and an open culture of experimentation. This research makes important theoretical contributions by illuminating AI’s transformative potential for internal communication. It also offers practical insights to guide organizations in strategically leveraging AI to elevate internal communication.

An overview of Applied Artificial Intelligence in Power Grids

Power grids are networks of lines and equipment used for electrical power generation, transmission, distribution, and consumption. Power grids are undergoing a process of digital transformation based on emerging information and communication technologies, with the aim of optimising the operation of power systems, increasing efficiency, reliability, sustainability, security, and quality of service, while reducing CO2 emissions. In this process of digital transformation, Artificial Intelligence (AI) has become a strategic element within power grids. This paper provides an overview of AI applications in power grids and how they can improve the efficiency, reliability, and availability of their processes. It analyses the main challenges and prospects for the future application of AI in power grids. AI applications help process large volumes of data and generate insights, with a focus on two primary functions: supporting operations and aiding strategic decision-making. These factors highlight the significant potential for AI application in power grids and solidify AI as a strategic key to the development of smart grids.

Machine learning, banking, industry, digitalization, innovation

Aim: The application of artificial intelligence (AI) offers an encouraging avenue for developments in the banking industry. Methods: The study adopted a scoping review of methodology to provide a comprehensive overview of relevant research regarding the potential uses and challenges of AI in banking. Consumer expectations have evolved, emphasizing features such as payment gateways, swift transactions, and resilient banking services. Results: Major utilization of the AI in banking industry includes enhanced customer engagement, satisfaction, and relationships, personalized financial guidance, smart digital wallets, regulation compliance and collaborative efforts, forecasting, tracking market trends, decision-making and problem-solving, anti-money laundering and fraud detection, automation of repetitive, large and time-intensive tasks, enhanced cybersecurity, and opportunity for digital financial inclusion. Conclusion: Users encounter challenges involving security issues, privacy concerns, and the complexity of technology in AI-driven banking services. Recommendation: While AI holds a crucial role in the banking industry, successful implementation demands ample allocation and consistent, predictable utilization of AI practices.

Application and Prospect of Artificial Intelligence Technology in Low-Carbon Cities—From the Perspective of Urban Planning Content and Process

In the era of digital transformation, artificial intelligence (AI) technology—one of the swiftest growing emerging technologies—when integrated with urban planning, can introduce innovative approaches for low-carbon city development and foster the attainment of dual carbon objectives: carbon neutrality and peak carbon emissions. Current research predominantly investigates the influence and alterations of emerging technologies on urban elements, yet it overlooks a comprehensive examination of the applicable procedures of these technologies and their potential synergy with urban planning. Consequently, this study employs a systematic literature review to delve into the application of AI in sectors such as architecture, transportation, land use, and green space development. It categorizes the specific impact processes into monitoring, identification, simulation, and prediction. By offering an exhaustive analysis of urban planning’s content and methodology, this paper elucidates the role of AI technology in the creation of low-carbon cities. The study found that: (1) Due to the varying degrees of application and integration with professional technologies in different fields, the current research focuses more on architecture, land use, and transportation. (2) Combining the four steps of urban planning, artificial intelligence can be divided into monitoring, recognition, simulation, and prediction types, each with its own characteristics. (3) Overall, AI technology is mainly applied in the identification and simulation of architecture, transportation, and land use. (4) There is still room for improvement in the application of AI technology in waste emissions and other algorithms.

ARTIFICIAL INTELLIGENCE IN CRIMINAL JUSTICE MANAGEMENT: A SYSTEMATIC LITERATURE REVIEW

This systematic review, based on 37 articles, explores the role of artificial intelligence (AI) in criminal justice, focusing on its applications in predictive policing, judicial risk assessments, and surveillance, as well as the associated ethical and regulatory challenges. AI has demonstrated substantial potential for improving efficiency and accuracy in criminal justice systems, from optimizing law enforcement resource allocation to providing data-driven risk assessments that support judicial decisions. However, the review identifies significant ethical issues, especially related to algorithmic bias, which can perpetuate existing societal inequalities and disproportionately affect marginalized communities. Concerns around transparency and accountability are prevalent, as the "black-box" nature of many AI algorithms complicates public understanding and trust in AI-driven outcomes. Surveillance tools, including facial recognition and behavioral analysis, enhance real-time threat detection but raise privacy and civil rights concerns, highlighting the need for regulatory oversight. Gaps in legal frameworks suggest the urgency for standardized policies that address data privacy, algorithmic fairness, and accountability in AI applications. The findings underscore that interdisciplinary collaboration, transparent practices, and comprehensive regulatory measures are essential to responsibly integrate AI into criminal justice, balancing technological advancements with justice, equity, and public trust.

Human-Machine Engagement (HME): Conceptualization, Typology of Forms, Antecedents, and Consequences

Artificial intelligence (AI) applications in customer-facing settings are growing rapidly. The general shift toward robot- and AI-powered services prompts a reshaping of customer engagement, bringing machines into engagement conceptualizations. In this paper, we build on service research around engagement and AI, incorporating computer science, and socio-technical systems perspective to conceptualize human-machine engagement (HME), offering a typology and nomological network of antecedents and consequences. Through three empirical studies, we develop a typology of four distinct forms of HME (informative, experimenting, praising, apprehensive), which differ in valence and intensity, underpinned by both emotional (excitement) and cognitive (concern, advocacy) drivers. We offer empirical evidence which reveals how these HME forms lead to different cognitive and personality-related outcomes for other users (perceived value of HME, perceived risk, affinity with HME) and service providers (willingness to implement in services, perceived value of HME). We also reveal how outcomes for service providers vary with the presence and absence of competitor pressure. Our findings broaden the scope of engagement research to include non-human actors and suggest both strategic and tactical guidance to service providers currently using and/or seeking to use generative AI (GenAI) in services alongside an agenda to direct future studies on HME.

Assessing the Impact of Generative Pre-Trained Transformers on AI Literacy and Public Awareness in India

Generative Pre-Trained Transformers (GPTs), a class of Artificial Intelligence (AI) models capable of producing human-like responses, have become widely adopted since their release. In India, this development raises questions about whether GPTs have influenced public awareness and interest in AI. Understanding this influence is crucial, particularly in the context of AI literacy. This study examines the impact of GPT adoption on creating increased awareness and public interest in AI in India. AI literacy is assessed using the four-factor framework proposed by Ng et al., (2021): knowledge and understanding of AI, uses and applications, evaluation and creation of new solutions, and ethical considerations. Information-seeking behavior serves as the theoretical foundation for examining changes in public interest across these dimensions. Google search volumes from 2020 to 2024 are analyzed to measure public engagement, with data divided into two periods: 2.5 years prior to and 1.5 years following the initial public release of ChatGPT in November 2022. Welch’s t-test is applied to assess changes in search volumes across these time periods. The results indicate a statistically significant increase in interest across all four aspects of AI literacy. The adoption of GPTs has significantly boosted public engagement with AI literacy in India, enhancing awareness and interest across all facets of AI knowledge and application.

The Application of Artificial Intelligence for the Design of a Virtual Assistant (Chatbot) as an innovative approach in user service: Social Security Center No.102

The "Social Security Center No.102" has a deficiency in customer service, which has caused problems such as low user satisfaction, bad reputation, long waiting times for answers and loss of customers. The objective of this project is to demonstrate that the design and implementation of a virtual assistant (Chatbot) optimizes the service to users of the organization. As for the design of the method, the research is considered mixed, applied and technological; and depending on the level of research, it is descriptive, explanatory, cross-sectional, non-experimental and field. A stratified population sample of 380 people was considered, to whom a "questionnaire" type data collection instrument was applied, consisting of 4 sections and 24 items. Based on the results obtained, it was shown that the Chatbot improved efficiency by 64.4% and effectiveness by 63.1% in the provision of customer service to users.

Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease

Abstract Purpose of Review Peripheral Artery Disease (PAD), a condition affecting millions of patients, is often underdiagnosed due to a lack of symptoms in the early stages and management can be complex given differences in genetic and phenotypic characteristics. This review aims to provide readers with an update on the utility of machine learning (ML) in the management of PAD. Recent Findings Recent research leveraging electronic health record (EHR) data and ML algorithms have demonstrated significant advances in the potential use of automated systems, namely artificial intelligence (AI), to accurately identify patients who might benefit from further PAD screening. Additionally, deep learning algorithms can be used on imaging data to assist in PAD diagnosis and automate clinical risk stratification. ML models can predict major adverse cardiovascular events (MACE) and major adverse limb events (MALE) with considerable accuracy, with many studies also demonstrating the ability to more accurately risk stratify patients for deleterious outcomes after surgical intervention. These predictions can assist physicians in developing more patient-centric treatment plans and allow for earlier, more aggressive management of modifiable risk-factors in high-risk patients. The use of proteomic biomarkers in ML models offers a valuable addition to traditional screening and stratification paradigms, though clinical utility may be limited by cost and accessibility. Summary The application of AI to the care of PAD patients may enable earlier diagnosis and more accurate risk stratification, leveraging readily available EHR and imaging data, and there is a burgeoning interest in incorporating biological data for further refinement. Thus, the promise of precision PAD care grows closer. Future research should focus on validating these models via real-world integration into clinical practice and prospective evaluation of the impact of this new care paradigm.

A review of artificial intelligence in wound care

Our aging population, diabetes, and obesity have fueled the growth of chronic wounds seen throughout the world. Often, wounds are a marker of poor health that leads to increased mortality rates. However, the diagnosis and treatment of these wounds are challenging. Incorrectly differentiating between chronic wounds and other complex conditions can lead to adverse events. Artificial intelligence (AI) has been shown to offer some early benefits, and we hypothesized that it may enhance wound care but also carry some notable risks. We performed a detailed search using PubMed, Scopus, Cumulated Index in Nursing and Allied Health Literature, and Web of Science for AI applications in wound care. AI was found to be applied to wound diagnosis and characterization, wound monitoring for tissue change, daily therapy, and prevention and prognostics. AI made for more efficient and accurate wound assessments, less painful assessments of chronic wounds, more personalized treatment, and improved prognostic prediction capabilities. AI also allowed for more precise at-home observation and care, facilitating earlier wound treatment as needed. Challenges associated with AI included how to best allocate AI-assisted technologies equitably, how to safely maintain patient data, and how to diversify datasets for algorithm training. Because the algorithms are not transparent, validating findings may be challenging. AI presents a powerful tool in several aspects of advanced wound care and has the potential to improve diagnoses, accelerate healing, reduce pain, and improve the cost-effectiveness of wound care. More research needs to be done into how to best incorporate AI into daily clinical practice while keeping clinicians aware of the potential risks of using these evolving technologies.

Operational Challenges and Prioritization of Potential Solutions for Integrating Vertiports into Airports

The integration of vertiports into airports for eVTOL/UAV flights poses operational challenges. The aim of the study was to propose and prioritize solutions to overcome these challenges. A comprehensive literature review identified remote vertiport networks, geofencing technology, dedicated airspace corridors, advanced collision avoidance systems, and dynamic airspace management as potential solutions. These solutions were prioritized using the Analytic Hierarchy Process (AHP) based on criteria such as safety, cost, efficiency, feasibility, and sustainability. Dynamic airspace management was the highest priority, followed by remote vertiport networks, dedicated airspace corridors, geofencing technology, and advanced collision avoidance systems. The study highlights the importance of dynamic data sharing and real-time planning through integrated ATM and UTM systems, enhanced by AI technologies, to ensure safety and efficiency. In addition, the development of remote vertiport networks and dedicated airspace corridors is essential to manage growing air traffic and ensure the safe coexistence of eVTOL/UAVs and traditional aircraft. Geofencing technology and advanced collision avoidance systems are also essential to maintain safety and operational integrity. It is recommended that future studies focus on the integration of ATM/UTM and the application of artificial intelligence. Continued collaboration between UAM stakeholders is essential to develop effective integration strategies.

AI AND MACHINE LEARNING IN BUSINESS PROCESS AUTOMATION: INNOVATING WAYS AI CAN ENHANCE OPERATIONAL EFFICIENCIES OR CUSTOMER EXPERIENCES IN U.S. ENTERPRISES

This study presents a comprehensive review of the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing business processes across various industries. By examining a total of 75 peer-reviewed articles, the review highlights key areas where AI and ML have demonstrated significant impact, including operational efficiency, customer engagement, and strategic decision-making. Findings indicate that AI-driven process optimizations, particularly through predictive maintenance and resource management, have led to substantial cost savings and improved productivity by minimizing downtime and enhancing workflow efficiencies. Additionally, AI’s ability to support personalized customer experiences—through recommendation systems, dynamic pricing, and chatbots—has proven instrumental in driving customer satisfaction, retention, and engagement, making it a critical tool in customer relationship management. Furthermore, the strategic adoption of AI and ML has enabled data-driven decision-making, allowing companies to respond more effectively to market changes and forecast business outcomes with greater accuracy. The study also explores the transition from Robotic Process Automation (RPA) to AI as a foundational step, illustrating how RPA provides a structured entry point for advanced AI applications, creating an automation-ready environment. However, challenges such as technical limitations, ethical concerns, and organizational resistance persist, underscoring the need for careful implementation strategies. Addressing these challenges will be vital for organizations aiming to maximize the benefits of AI-driven automation in an increasingly competitive digital landscape. This review underscores the significant potential of AI and ML in reshaping business models and emphasizes the importance of ongoing research and development in these fields to support sustainable and scalable innovations.

Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.

In recent years, the integration of artificial intelligence (AI) techniques into medical imaging has shown great potential to transform the diagnostic process. This review aims to provide a comprehensive overview of current state-of-the-art applications for AI in abdominal and pelvic ultrasound imaging. We searched the PubMed, FDA, and ClinicalTrials.gov databases for applications of AI in abdominal and pelvic ultrasound imaging. A total of 128 titles were identified from the database search and were eligible for screening. After screening, 57 manuscripts were included in the final review. The main anatomical applications included multi-organ detection (n = 16, 28%), gynecology (n = 15, 26%), hepatobiliary system (n = 13, 23%), and musculoskeletal (n = 8, 14%). The main methodological applications included deep learning (n = 37, 65%), machine learning (n = 13, 23%), natural language processing (n = 5, 9%), and robots (n = 2, 4%). The majority of the studies were single-center (n = 43, 75%) and retrospective (n = 56, 98%). We identified 17 FDA approved AI ultrasound devices, with only a few being specifically used for abdominal/pelvic imaging (infertility monitoring and follicle development). The application of AI in abdominal/pelvic ultrasound shows promising early results for disease diagnosis, monitoring, and report refinement. However, the risk of bias remains high because very few of these applications have been prospectively validated (in multi-center studies) or have received FDA clearance.

Application of artificial intelligence in career development of women in STEM field: Counseling, network construction and recruitment

With the rapid development of science and technology, the application of artificial intelligence (AI) in STEM (science, technology, engineering and mathematics) is increasingly extensive, which provides a new idea for solving the challenges of women's career development in STEM. This paper deeply discusses the application of AI in counseling, network construction and recruitment of women's STEM career development, aiming at promoting gender equality and women's career growth. In career counseling, AI technology provides customized career advice and development path for women through deep learning and big data analysis. According to personal education background, skill level, hobbies and career goals, the system intelligently recommends suitable career directions, and updates industry trends and job market data in real time to help women make more informed career choices. The AI tutoring system also enhances women's professional self-confidence through functions such as simulated interviews and career planning exercises. In terms of network construction, AI helps women to expand their professional networks more effectively through intelligent recommendation system. Based on user behavior data and preferences, AI accurately recommends industry seminars, technical exchange activities and online communities to promote women to establish contacts with like-minded professionals. AI also assists women in managing and maintaining network relationships, ensures the continuity of network interaction through intelligent reminding functions, and identifies key nodes and influential people to enhance network quality. In the recruitment process, AI technology reduces gender bias and improves the fairness and transparency of recruitment through automatic resume screening, standardized interview process and competency-based evaluation model. AI system can analyze a large amount of data, accurately match the skills and job requirements of female candidates, and improve the employment rate of women in STEM field. At the same time, through transparent algorithm decision-making process and fairness check, the potential gender bias problem in AI algorithm is overcome. The research in this paper provides theoretical support and practical guidance for using AI technology to promote women's career development in STEM field, and helps to build a more equal and inclusive scientific and technological working environment.

Application of Artificial Intelligence in Library and Information Centres: A Study

Application of Artificial Intelligence in Library and Information Centres: A Study

Recent Developments and Applications of Artificial Intelligence in Solid/Liquid Extraction Studies

Recent Developments and Applications of Artificial Intelligence in Solid/Liquid Extraction Studies