Artificial Machine Research Articles

Novel applications of artificial intelligence, machine learning, and deep learning-based modalities in dental traumatology: An overview of evidence-based literature

Novel applications of artificial intelligence, machine learning, and deep learning-based modalities in dental traumatology: An overview of evidence-based literature

Towards a QBLM-Based Qualification-Management Methodology Supporting Human-Resource Management and Development

This position paper presents a novel perspective on addressing the challenges of digital transformation in higher education through the development of a qualification-based learning model (QBLM) qualification management methodology. It argues that the rapid pace of technological advancement and the resulting need for continuous upskilling and reskilling necessitate a more dynamic and adaptive approach to human-resource management and development. The paper posits that by extending QBLM through the integration of artificial intelligence (AI) and machine learning (ML), a more effective system for analyzing competence requirements and designing personalized learning pathways can be created. The paper proposes a three-fold approach: (1) developing the FPHR ontology to support semantic annotation of HR qualifications in higher-education institutions (HEIs), (2) integrating this ontology into QBLM to ensure the machine-readability of qualifications, and (3) modeling a knowledge-based production process for HRs in skills-based learning. This paper outlines the current state of the art, presents conceptual models, and describes planned proof-of-concept implementations and evaluations. It contends that this approach will significantly enhance the effectiveness of human-resource development in the rapidly evolving digital knowledge society. By presenting this position, the paper aims to stimulate discussion and collaboration within the academic community on innovative approaches to qualification management in higher education. The work addresses critical issues arising from technological development and offers a forward-thinking solution to bridge the gap between current and future skill requirements in industry and academia.

Evaluating the impact of AI and blockchain on credit risk mitigation: A predictive analytic approach using machine learning

In recent years, the integration of artificial intelligence (AI), blockchain technology, and machine learning has transformed credit risk mitigation strategies in the financial industry. This paper explores the practical applications of these technologies in identifying, assessing, and managing credit risk, with a specific focus on predictive analytics and decentralized frameworks. Through a comprehensive literature review and case studies, the research demonstrates how AI-driven algorithms, blockchain's transparent and immutable ledger systems, and machine learning models have enhanced the precision and efficiency of credit risk evaluations. Additionally, the study investigates how these innovations are being adopted by financial institutions to create more accurate credit scoring systems, reduce fraud, and optimize operational risk management. While these technologies hold great promise, challenges such as data privacy, regulatory compliance, and implementation costs remain significant barriers. The paper concludes with recommendations for overcoming these challenges and maximizing the potential of AI, blockchain, and machine learning in credit risk mitigation.

Leveraging AI and ML for Next-Generation Cloud Security: Innovations in Risk-Based Access Management

In the increasing reliance on the cloud computing era, securing digital assets against sophisticated cyber threats has become a critical concern for organizations globally. Traditional security mechanisms, which often rely on static and pre-defined access control policies, must be revised to address these threats' dynamic and evolving nature. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing cloud security through the development of advanced Risk-Based Access Management (RBAM) systems. The primary objective is to evaluate how AI and ML can improve dynamic access control, threat prediction, and mitigation strategies within cloud environments. The research adopts a mixed-methods approach, combining quantitative analysis of RBAM system performance with qualitative insights from cybersecurity experts. AI/ML models were developed using extensive historical access log datasets and integrated into a cloud-based RBAM prototype. The system's performance was assessed based on its accuracy in threat detection, reduction in false positives, and effectiveness in dynamically adjusting access controls. Results indicate that the AI-enhanced RBAM system significantly outperforms traditional methods, achieving a 30% reduction in false positives and a 25% decrease in unauthorized access incidents. Additionally, AI-driven threat prediction models demonstrated high accuracy, enabling preemptive actions to mitigate potential security breaches. These findings highlight the transformative potential of AI and ML in cloud security, providing a more adaptive and proactive defense against emerging threats. The study concludes with recommendations for refining AI/ML models and exploring their application in other areas of cloud security, emphasizing the need for continued innovation to safeguard the increasingly complex digital landscapes that organizations operate within today.

Setting new benchmarks for combating financial crimes and ensuring the safety and security of America's digital financial landscape

The increasing digitalization of financial services in the United States has introduced significant challenges in combating financial crimes, including fraud, money laundering, and cybercrimes. As digital transactions become more pervasive, so too do the opportunities for illicit activities that exploit the vulnerabilities of these systems. This research seeks to establish new benchmarks for combating financial crimes and ensuring the security of America's digital financial landscape. By leveraging advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), and blockchain, this study explores innovative approaches to enhancing fraud detection, improving transparency, and reducing the risk of money laundering. A mixed-methods approach was employed, combining qualitative analysis of existing regulatory frameworks with quantitative case studies to assess the effectiveness of these technologies in real-world applications. The results indicate that AI and ML significantly enhance financial institutions' ability to detect fraudulent activities in real-time, reducing false positives and allowing for more efficient resource allocation. Furthermore, blockchain technology improved financial transactions' traceability, enhancing financial systems' transparency and accountability. The study concludes with recommendations for policy and practice, emphasizing the need for adaptive regulatory frameworks, increased investment in advanced technologies, and more vital collaboration between the public and private sectors. These findings suggest that a proactive approach, integrating technological innovation with strategic policy measures, is crucial for safeguarding America's financial infrastructure against emerging threats in the digital era.

A Survey on Role of Artificial Intelligence and Internet of Things in Sericulture

This paper takes a deep dive into the captivating world of sericulture and the unique varieties of silk that have graced humanity throughout history. The work carried out in the paper provides an insight of exploring the different types of silk, including Muga Silk, Tussar Silk, Mulberry Silk and Eri Silk. Each of these silk has its own distinct qualities, weaving a rich tapestry of textures, colors and cultural significance that has enthralled people for generations. As the technological advancements have provided the interdisciplinary approach of understanding the role of artificial intelligence, machine learning and deep learning approaches in the area of sericulture, wide verities of techniques, methods and analysis are available in the literature. The paper aims at presenting an exhaustive literature survey on using AI based methods, applications in sericulture. In essence, this paper showcases the beautiful synergy of tradition and technology in sericulture

GFRP-Reinforced Concrete Columns: State-of-the-Art, Behavior, and Research Needs

This comprehensive review paper delves into the utilization of Glass Fiber-Reinforced Polymer (GFRP) composites within the realm of concrete column reinforcement, spotlighting the surge in structural engineering applications that leverage GFRP instead of traditional steel to circumvent the latter’s corrosion issues. Despite a significant corpus of research on GFRP-reinforced structural members, questions about their compression behavior persist, making it a focal area of this review. This study evaluates the properties of GFRP bars and their impact on the structural behavior of concrete columns, addressing variables such as concrete type and strength, cross-sectional geometry, slenderness ratio, and reinforcement specifics under varied loading protocols. With a dataset spanning over 250 publications from 1988 to 2024, our findings reveal a marked increase in research interest, particularly in regions like China, Canada, and the United States, highlighting GFRP’s potential as a cost-effective and durable alternative to steel. However, gaps in current knowledge, especially concerning Ultra-High-Performance Concrete (UHPC) reinforced with GFRP, underscore the necessity for targeted research. Additionally, the contribution of GFRP rebars to compressive column capacity ranges from 5% to 40%, but current design codes and standards underestimate this, necessitating new models and design provisions that accurately reflect GFRP’s compressive behavior. Moreover, this review identifies other critical areas for future exploration, including the influence of cross-sectional geometry on structural behavior, the application of GFRP in seismic resistance, and the evaluation of the size effect on column strength. Furthermore, the paper calls for advanced studies on the long-term durability of GFRP-reinforced structures under various environmental conditions, environmental and economic impacts of GFRP usage, and the potential of Artificial Intelligence (AI) and Machine Learning (ML) in predicting the performance of GFRP-reinforced columns. Addressing these research gaps is crucial for developing more resilient and sustainable concrete structures, particularly in seismic zones and harsh environmental conditions, and fostering advancements in structural engineering through the adoption of innovative, efficient construction practices.

Combating terrorist financing in cryptocurrency platforms: The role of AI and machine learning

This study addresses the critical issue of terrorist financing through cryptocurrency platforms, a growing concern due to digital currencies' pseudonymous nature and global reach. The research explores the strategic role of Artificial Intelligence (AI) and Machine Learning (ML) in identifying, preventing, and disrupting the flow of illicit funds used to finance terrorism. Employing a mixed-methods approach, the study integrates qualitative case studies of documented instances of cryptocurrency-based terrorist financing with quantitative data analysis from significant cryptocurrency exchanges. Advanced AI and ML algorithms, including supervised learning models such as decision trees and neural networks, were applied to detect suspicious transactions indicative of terrorist activities. The findings reveal that AI and ML technologies significantly enhance the ability to identify patterns of terrorist financing within large and complex datasets, with models achieving precision and recall rates exceeding 90%. However, challenges remain, particularly regarding the quality and standardization of data across platforms, algorithmic biases, and the need for continuous updates to counter evolving tactics used by terrorist organizations. The study concludes that AI and ML present powerful tools for enhancing financial security. However, their successful implementation requires overcoming these challenges through collaborative efforts among stakeholders, including financial institutions, regulators, and technology providers. This research contributes to the growing field of economic crime prevention by offering a robust framework for integrating AI-driven solutions into the fight against terrorist financing on cryptocurrency platforms.

IMPACT OF DIGITALISATION ON BANKING SECTOR

The banking sector has been substantially changed by the fast development of digital technology. The effect of digitalisation on general financial services, client contacts, and banking operations is investigated in this article. In banking, digitalisation has brought about the automation of formerly manual procedures, therefore improving client happiness, efficiency, and operating expenses while lowering running costs. Offering 24/7 access to financial services from anywhere in the globe, services such internet banking, smartphone apps, and digital payment systems have transformed the way consumers connect with banks. Furthermore, digitalisation has helped fintech to emerge, therefore undermining established banking structures and encouraging more innovation and competitiveness. Advanced technologies such artificial intelligence (AI), machine learning (ML), and blockchain have been embraced by banks to guarantee better security in transactions, provide tailored services, and help to guide decisions. Although digitalisation has many advantages, it also brings problems like cybersecurity threats, data privacy issues, and the necessity of large technological infrastructure investment. This study also addresses its function in financial inclusion by enabling underprivileged groups to use banking services. Banks have to strike a mix between innovation and regulatory compliance, risk control, and client confidence as the digital revolution unfolds. The results of this research imply that banks must embrace digital transformation if they are to stay competitive in an economy becoming more and more digital and also meet the changing demands of tech-savvy customers.

Detection of the carcinogenic liver fluke, Opisthorchis viverrini: comparison of two coprological methods versus the automatic feces analyzer.

Liver fluke infection, particularly Opisthorchis viverrini, poses a significant public health risk in Thailand, where it is closely associated with cholangiocarcinoma and contributes to substantial mortality in the northeastern region. Diagnosis of this condition employs various parasitological approaches. This research aims to compare the diagnostic accuracy of three parasitological techniques: the Kato Katz technique (KKT), the formalin-ethyl acetate concentration technique (FECT), and the Fully Automatic Feces Analyzer (FAFA) for O. viverrini identification. A total of 455 fecal specimens were collected from rural areas across five provinces in northeastern Thailand. The specimens were processed according to each method and examined through microscopy for KKT and FECT, and by utilizing an artificial intelligence-based machine for FAFA. Data analysis was conducted to assess parasitic infection rates and observe diagnostic accuracy. The results revealed a parasitic infection rate of 19.34%, with the majority of infections attributed to O. viverrini (18.02%), followed by Strongyloides stercoralis (0.88%). FECT exhibited the highest positive detection of O. viverrini eggs (16.48%), followed by FAFA (10.55%), and KKT (8.57%), respectively. Statistical analysis indicated sensitivity and specificity values for O. viverrini detection by KKT (100% and 89.21%), FECT (98.67% and 97.63%), and FAFA (97.92% and 91.15%). The positive predictive value, negative predictive value, and kappa were reported for FECT (89.16%, 99.73%, 0.92), FAFA (56.63%, 99.73%, 0.67), and KKT (45.78%, 100%, 0.58). Additionally, the preparation time for KKT, FECT, and FAFA was 30, 15, and 10 min, respectively. In conclusion, this study highlights FECT, KKT, and FAFA as comparably sensitive in diagnosing O. viverrini. The FAFA machine emerges as a potentially valuable tool for detecting O. viverrini and other parasitic infections, showcasing promise for clinical use. The findings provide valuable insights into the diagnostic landscape and underscore the potential of FAFA in enhancing efficiency and accuracy in parasitological assessments.

The future of software development: integrating AI and machine learning into front-end technologies

This paper explores the integration of artificial intelligence (AI) and machine learning into front-end development, highlighting the transformative potential these technologies hold for the future of software development. By automating routine tasks, enhancing predictive capabilities, and improving user engagement, AI and machine learning are set to revolutionize how digital applications are created and experienced. However, this integration is accompanied by significant technical challenges, including the complexity of AI systems, performance concerns, and ethical issues such as data privacy and bias. The paper also discusses current applications and emerging trends in AI-powered front-end technologies, providing a comprehensive overview of the benefits and obstacles. It emphasizes the importance of ongoing research, innovation, and ethical considerations in maintaining the U.S. tech sector's competitive edge in this rapidly evolving field. The findings suggest that while AI and machine learning offer substantial opportunities for innovation, careful implementation is essential to ensure these technologies contribute positively to the future of software development.

Adaptive Cybersecurity in the Digital Age: Emerging Threat Vectors and Next-Generation Defense Strategies

Abstract: This article examines the rapidly evolving landscape of cybersecurity, focusing on emerging threats and innovative defense mechanisms. We analyze four key threat vectors: Advanced Persistent Threats (APTs), ransomware, Internet of Things (IoT) vulnerabilities, and social engineering attacks. These threats pose significant risks to organizations, including data breaches, financial losses, and operational disruptions. In response, we explore cutting-edge defense mechanisms such as Artificial Intelligence and Machine Learning for threat detection, Zero Trust Architecture for access control, blockchain for data integrity, behavioral analytics for insider threat detection, and quantum cryptography for enhanced encryption. The article also outlines best practices for cybersecurity, including regular software updates, strong password policies, employee training, and security audits. By synthesizing current research and industry trends, this article provides a comprehensive overview of the cybersecurity landscape, emphasizing the need for continuous adaptation and vigilance in the face of evolving cyber threats

Holistic learning and optimization scheme for wireless networks

AbstractFuture networks are expected to use Artificial Intelligence and machine learning applications intensely. The primary focus of machine learning techniques will be on intelligent automation and decision‐making based on information learned from network functions, resources, and users. The research community has studied several schemes for using machine learning in communications and networks in the last two decades. However, the schemes mainly focus on learning the state information of individual network functions and resources and performing their optimization in a standalone fashion. Recently, a study group from the International Telecommunication Union has outlined machine learning pipeline requirements. It addresses several aspects of machine learning operations, such as model selection, data sources, and action points. However, the mechanisms to define the state of network functions and resources for different network layers and domains are yet to be explored. Therefore, in addition to the above, specific studies are required for cross‐layer and administrative domain knowledge sharing and holistic optimization. Without considering the holistic view, applying machine learning to optimizing individual functions and resources may result in nonoptimal and unexpected behaviors instead of having any positive effect. The need for global and deep holistic learning has recently been identified in the literature; however, no implementation of the above scheme has been studied or evaluated for networks. This article proposes a novel approach, implementing global and deep holistic learning in wireless networks toward fulfilling the requirements of future intelligent networks. It also proposes an objective function‐based feature engineering for wireless networks. Experimental results of the proposed scheme show significant improvements in the network performance and accuracy of the machine learning models. Moreover, applications of transfer learning to base learners for network functions and resources can significantly expedite decision‐making with reduced computational costs.

Evaluating a Multidisciplinary Model for Managing Human Uncertainty in 5G Cyber–Physical–Social Systems

This paper presents a comprehensive evaluation of the previously introduced multidisciplinary model to quantify human uncertainty (MMtQHU) within a realistic 5G-enabled cyber–physical–social systems (CPSS) environment. The MMtQHU, which integrates human, social, and environmental factors into CPSS modeling, is applied to the Ingolstadt traffic scenario (InTAS), a detailed urban simulation reflecting high-traffic conditions. By modeling unpredictable driver behaviors, such as deviations from optimal routes, the study assesses the model’s effectiveness in managing human-induced uncertainties in vehicle-for-hire (VFH) applications. The evaluation shows that human uncertainty significantly impacts 5G network resource allocation and traffic dynamics. A comparative analysis of traditional resource allocation methods reveals their limitations in handling the dynamic nature of human behavior. These findings underscore the necessity for advanced, adaptive strategies, potentially leveraging artificial intelligence and machine learning to enhance the resilience and efficiency of 5G networks in CPSS environments. The study offers valuable insights for future advancements in robust and adaptive 5G infrastructure by highlighting the critical role of integrating human behavior into CPSS models.

Editorial: Applications of Artificial Intelligence and Machine Learning in Games

Recent developments in AI and machine learning technologies have significantly expanded their application area in games [...]

An Evaluation of the Maternal Patient Experience through Natural Language Processing Techniques: The Case of Twitter Data in the United States during COVID-19

The healthcare sector constantly investigates ways to improve patient outcomes and provide more patient-centered care. Delivering quality medical care involves ensuring that patients have a positive experience. Most healthcare organizations use patient survey feedback to measure patients’ experiences. However, the power of social media can be harnessed using artificial intelligence and machine learning techniques to provide researchers with valuable insights into understanding patient experience and care. Our primary research objective is to develop a social media analytics model to evaluate the maternal patient experience during the COVID-19 pandemic. We used the “COVID-19 Tweets” Dataset, which has over 28 million tweets, and extracted tweets from the US with words relevant to maternal patients. The maternal patient cohort was selected because the United States has the highest percentage of maternal mortality and morbidity rate among the developed countries in the world. We evaluated patient experience using natural language processing (NLP) techniques such as word clouds, word clustering, frequency analysis, and network analysis of words that relate to “pains” and “gains” regarding the maternal patient experience, which are expressed through social media. The pandemic showcased the worries of mothers and providers on the risks of COVID-19. However, many people also shared how they survived the pandemic. Both providers and maternal patients had concerns regarding the pregnancy risks due to COVID-19. This model will help process improvement experts without domain expertise to understand the various domain challenges efficiently. Such insights can help decision-makers improve the patient care system.

ML-Based Maintenance and Control Process Analysis, Simulation, and Automation—A Review

Automation and digitalization in various industries towards the Industry 4.0/5.0 paradigms are rapidly progressing thanks to the use of sensors, Industrial Internet of Things (IIoT), and advanced fifth generation (5G) and sixth generation (6G) mobile networks supported by simulation and automation of processes using artificial intelligence (AI) and machine learning (ML). Ensuring the continuity of operations under different conditions is becoming a key factor. One of the most frequently requested solutions is currently predictive maintenance, i.e., the simulation and automation of maintenance processes based on ML. This article aims to extract the main trends in the area of ML-based predictive maintenance present in studies and publications, critically evaluate and compare them, and define priorities for their research and development based on our own experience and a literature review. We provide examples of how BCI-controlled predictive maintenance due to brain–computer interfaces (BCIs) play a transformative role in AI-based predictive maintenance, enabling direct human interaction with complex systems.

Análisis de la efectividad de phishing automático

The article examines the growing threat of automated phishing, a social engineering technique that has evolved with automation, complicating the protection of computer systems both in Ecuador and globally. The objective is to analyze the effectiveness of this technique and its impact on information security. The methodology used includes a systematic review of the literature on automated phishing, highlighting how automation has increased the sophistication and scope of these attacks. The results show that automated attacks, facilitated by artificial intelligence and machine learning, have surpassed traditional attacks in frequency globally. In Ecuador, although less common, automated attacks have had a notable impact, especially in sectors with limited security measures. The findings highlight the need to adopt robust security controls, such as multi-factor authentication and ongoing cybersecurity training, in addition to following international standards such as ISO/IEC 27001 to mitigate the risks of automated phishing. Awareness talks are also recommended to strengthen security against these threats.

Будущее уголовного правосудия: роль искусственного интеллекта в предиктивной аналитике

Introduction. This article focuses on the importance and prospects for the use of artificial intelligence in predictive analytics in the criminal justice context. The research is motivated by the significant development of artificial intelligence and machine learning technologies, which are being used in a multitude of fields, including criminal justice. The authors detail the theoretical and practical aspects of predictive analytics, which makes it possible to predict future events based on statistical data and machine learning algorithms. Special attention is paid to the difference between artificial intelligence and predictive analytics. The effectiveness of the application of predictive analytics in criminal justice, including optimising preliminary investigations, improving criminal prosecution and predicting the outcome of criminal cases, is highlighted. Methods. The basis of the research methodology is dialectical materialism, applied general scientific (system-structural and formallogical, inductive and deductive, analysis and synthesis) and special (formal-legal, comparativelegal) methods. Results. The authors conclude that artificial intelligence spans a wider range of tasks requiring human intelligence, while predictive analytics concentrates on making predictions. Advanced technologies that are already in active use in various countries, improving and optimising the allocation of law enforcement and judicial resources, are described. The prospect of integrating virtual and augmented reality technologies into criminal justice is considered, which can radically change approaches to predictive analytics and criminal procedure in general, enriching visualisation and interactive cooperation between participants of legal relations.

An automated social media manager based on artificial intelligence

In today's world of social media, where brands and companies constantly interact with their audiences, the automation of content management processes is becoming increasingly important. Manual content creation, publication, and analysis on social networks require significant resources and time. Automated social media management systems based on artificial intelligence help simplify these processes, enhancing efficiency and optimizing audience engagement strategies. Artificial intelligence in social media marketing systems allows for the automation of tasks such as creating text posts, scheduling publications across different platforms, and analyzing user interactions. Artificial intelligence can analyze audience behavior, predict the optimal time for posts, and recommend changes to content strategies for maximum reach. This not only saves time but also improves the effectiveness of marketing campaigns through more accurate analytical data. However, the automation of social media marketing also faces challenges. The primary issues include the complexity of integrating different social networks into a single management system and the limitations in generating high-quality content that meets the emotional and stylistic requirements of the brand. Additionally, a major challenge is content personalization based on data analysis, which must account for each audience segment's unique needs and behavioral characteristics. Despite these challenges, the prospects for developing automated social media marketing managers are promising due to ongoing innovations in artificial intelligence and machine learning. Future research and development will focus on improving the quality of automated content generation, enhancing user interfaces, and integrating new social platforms to expand multi-channel marketing campaign management capabilities. Thus, the automation of social media marketing is a promising field that will enable brands and companies to optimize their audience engagement strategies while significantly reducing the time and resources required for content creation, publication, and analysis. This work presents the structure of the main entities of an automated social media management system based on artificial intelligence, including users, professions, questions, answers, settings, and integration with Telegram. The developed system enables efficient content management, automates publications, and personalizes audience interaction based on user responses. The system's described key components, attributes, and relationships provide scalability and flexibility, allowing brands and companies to optimize their marketing strategies and client interactions.