Value Of Artificial Intelligence Research Articles

Artificial Interpretation: An Investigation into the Feasibility of Archaeologically Focused Seismic Interpretation via Machine Learning

The value of artificial intelligence and machine learning applications for use in heritage research is increasingly appreciated. In specific areas, notably remote sensing, datasets have increased in extent and resolution to the point that manual interpretation is problematic and the availability of skilled interpreters to undertake such work is limited. Interpretation of the geophysical datasets associated with prehistoric submerged landscapes is particularly challenging. Following the Last Glacial Maximum, sea levels rose by 120 m globally, and vast, habitable landscapes were lost to the sea. These landscapes were inaccessible until extensive remote sensing datasets were provided by the offshore energy sector. In this paper, we provide the results of a research programme centred on AI applications using data from the southern North Sea. Here, an area of c. 188,000 km2 of habitable terrestrial land was inundated between c. 20,000 BP and 7000 BP, along with the cultural heritage it contained. As part of this project, machine learning tools were applied to detect and interpret features with potential archaeological significance from shallow seismic data. The output provides a proof-of-concept model demonstrating verifiable results and the potential for a further, more complex, leveraging of AI interpretation for the study of submarine palaeolandscapes.

An efficient visual servo tracker for herd monitoring by UAV

It is a challenging and meaningful task to carry out UAV-based livestock monitoring in high-altitude (more than 4500 m on average) and cold regions (annual average – 4 °C) on the Qinghai Tibet Plateau. The purpose of artificial intelligence (AI) is to execute automated tasks and to solve practical problems in actual applications by combining the software technology with the hardware carrier to create integrated advanced devices. Only in this way, the maximum value of AI could be realized. In this paper, a real-time tracking system with dynamic target tracking ability is proposed. It is developed based on the tracking-by-detection architecture using YOLOv7 and Deep SORT algorithms for target detection and tracking, respectively. In response to the problems encountered in the tracking process of complex and dense scenes, our work (1) Uses optical flow to compensate the Kalman filter, to solve the problem of mismatch between the target bounding box predicted by the Kalman filter (KF) and the input when the target detection in the current frame is complex, thereby improving the prediction accuracy; (2) Using a low confidence trajectory filtering method to reduce false positive trajectories generated by Deep SORT, thereby mitigating the impact of unreliable detection on target tracking. (3) A visual servo controller has been designed for the Unmanned Aerial Vehicle (UAV) to reduce the impact of rapid movement on tracking and ensure that the target is always within the field of view of the UAV camera, thereby achieving automatic tracking tasks. Finally, the system was tested using Tibetan yaks on the Qinghai Tibet Plateau as tracking targets, and the results showed that the system has real-time multi tracking ability and ideal visual servo effect in complex and dense scenes.

Prediction model based on artificial intelligence for identifying risk of coronary atherosclerotic heart disease in computed tomography

ObjectiveTo analyze the application value of artificial intelligence (AI) in coronary computed tomography angiography (CCTA) image processing and diagnosis of coronary atherosclerotic heart disease (CHD). MethodsA total of 80 patients with suspected CHD in our hospital were selected for CCTA examination and blood lipid examination. The convolutional neural networks (CNN) model of coronary artery plaque detection was constructed, and the data set was randomly divided into training set and test set after pretreatment of lipid characteristics and image characteristics. The prediction efficiency and accuracy of the model were evaluated. ResultsIn the data set, the lipid indexes LDL-C, TC, and TG of patients in the CHD group were significantly higher than those in the Non-CHD group (P < 0.05). The average processing and diagnosis time of the AI model was (187.19 ± 18.79) s, which was significantly shorter than the average time of doctors (989.07 ± 50.40) s, and the difference was statistically significant (P < 0.05). There was no significant difference in the detection of calcified plaque, non-calcified plaque, and mixed plaque between doctors and AI models (P > 0.05). However, 5 plaques were misdiagnosed in the AI model (3.38%). The area under the curve (AUC) value of the CNN recognition model-based AI and manual recognition of doctors for the CHD were 0.870 (95% CI: 0.698–0.931) and 0.870 (95% CI: 0.691–0.926) (P < 0.001). ConclusionAI integrated with lipid parameters has certain clinical value in CCTA image processing efficiency and plaque diagnosis, and can be used as an effective auxiliary tool to analyze and diagnose CHD.

An elemental ethics for artificial intelligence: water as resistance within AI’s value chain

AbstractResearch and activism have increasingly denounced the problematic environmental record of the infrastructure and value chain underpinning artificial intelligence (AI). Water-intensive data centres, polluting mineral extraction and e-waste dumping are incontrovertibly part of AI’s footprint. In this article, I turn to areas affected by AI-fuelled environmental harm and identify an ethics of resistance emerging from local activists, which I term ‘elemental ethics’. Elemental ethics interrogates the AI value chain’s problematic relationship with the elements that make up the world, critiques the undermining of local and ancestral approaches to nature and reveals the vital and quotidian harms engendered by so-called intelligent systems. While this ethics is emerging from grassroots and Indigenous groups, it echoes recent calls from environmental philosophy to reconnect with the environment via the elements. In empirical terms, this article looks at groups in Chile resisting a Google data centre project in Santiago and lithium extraction (used for rechargeable batteries) in Lickan Antay Indigenous territory, Atacama Desert. As I show, elemental ethics can complement top-down, utilitarian and quantitative approaches to AI ethics and sustainable AI as well as interrogate whose lived experience and well-being counts in debates on AI extinction.

LIS Educators’ Perception Towards the Adoption of AI Tools in Nigerian Library Schools

The incorporation of Artificial Intelligence (AI) into education marks a significant shift in how students learn, teachers teach, and educational institutions operate. This research delved into the knowledge and views of Library and Information Science (LIS) educators regarding the use of AI in library schools in Rivers State. The study employed a survey approach, combining qualitative and quantitative methods. A total of 44 LIS educators from various institutions in Rivers State participated, selected through random sampling, and data were collected using an online survey. The study found that while many LIS educators are aware of AI and have integrated it into their teaching and research, there remains a considerable gap in formal training and professional development in this area. Despite this, there is a clear understanding among educators of the value of AI in library and information science education, in line with broader trends in education and industry. The research also identified positive attitudes towards AI as a tool to enhance education quality and prepare students for careers in librarianship and information science. However, several barriers hinder the integration of AI into curricula and practices, including lecturer attitudes, credibility of information sources, internet connectivity, negative institutional perceptions, and low lecturer competency in AI. To address these challenges, the study recommends that Nigerian library schools fully implement AI technologies like chatbots, barcodes, RFIDs, and robotics to enhance teaching activities. It also suggests that higher education institutions develop specialized training programs and workshops on AI for library schools, covering both basic and advanced concepts. This would enable educators to effectively integrate AI into their teaching and research practices.

AI Value Creation: Operational value creation potential of semantic AI assistants in corporate training

This paper explores the potential value of semantic artificial intelligence (AI) in corporate training based on a study of semantic AI learning assistants in secondary schools and interviews with human resource professionals. The findings suggest that semantic AI can significantly improve learning outcomes and streamline corporate training processes through personalized, on-demand learning. However, successful implementation requires addressing privacy, security, and user acceptance challenges. While benefits such as efficiency, cost savings, and performance gains are achievable, value creation depends on the organizational context. Further research should explore the long-term impact of semantic AI in corporate learning.

AI in risk management: An analytical comparison between the U.S. and Nigerian banking sectors

This paper presents a comprehensive review of the application and impact of Artificial Intelligence (AI) in risk management within the banking sectors of the United States and Nigeria, emphasizing a comparative analysis. The objective is to assess how AI technologies are adopted and implemented in risk management practices across these diverse banking environments, identifying the benefits achieved and the challenges faced. The review synthesizes existing literature, including case studies, industry reports, and academic research, to outline the current state of AI in risk management. It delves into various risk types such as credit, market, operational, and compliance risks, exploring the specific AI tools and techniques employed to address these risks in each country. Key findings suggest that U.S. banks have a more mature implementation of AI in risk management, characterized by the adoption of advanced analytics, machine learning models, and natural language processing for enhanced decision-making, fraud detection, and compliance monitoring. In contrast, the Nigerian banking sector is at a nascent stage of AI adoption, with efforts hampered by challenges like inadequate technological infrastructure, regulatory hurdles, and a lack of skilled professionals in AI. Despite these differences, the paper identifies a strong interest and potential for growth in AI applications within the Nigerian banking sector, spurred by an increasing recognition of AI's value in enhancing competitiveness and meeting regulatory demands. Conclusively, the review underscores the critical role of supportive regulatory policies, investment in technological infrastructure, and capacity building in human capital as pivotal elements for fostering the effective integration of AI in risk management. The comparative analysis reveals both the disparities and potential areas of collaboration between the U.S. and Nigerian banking sectors, advocating for a global dialogue on best practices and strategies for AI adoption in risk management.

Prediction and optimization of civil engineering material properties based on artificial intelligence

In the modern research field of predicting and optimizing the performance of civil engineering materials, the incorporation of artificial intelligence (AI) has provided a new perspective. The research is based on a thorough examination of the central role of AI technology and the application of different mathematical models in the task of material performance prediction and optimization, summarizing the current state of AI technology application in the field of civil engineering material performance prediction. Against this backdrop, the research further proposes a brand-new AI-based civil engineering material performance prediction method. This study, aiming to realize the optimization of material performance prediction tasks, utilized cutting-edge machine learning techniques and mathematical models, proposing this new AI prediction framework. Detailed discussions on design, implementation, and evaluation were conducted in the forecast framework, which includes a large number of tables, mathematical functions, and references. Through rigorous study and implementation, the prediction framework exhibited admirable performance, demonstrating extensive applicability for civil engineering material performance prediction and optimization. In conclusion, this new AI-based civil engineering material performance prediction method has demonstrated encouraging results. This study provides a new horizon, illustrating the tremendous potential and value of AI for predicting the performance of civil engineering materials, thereby improving efficiency and effectiveness in the civil engineering field. Overall, this new AI prediction method provides a fresh pathway for future research, making an essential contribution to anticipating and optimizing in civil engineering field.

Automatic detection of thyroid nodules with a real-time artificial intelligence system in a real clinical scenario and the associated influencing factors.

At present, most articles mainly focused on the diagnosis of thyroid nodules by using artificial intelligence (AI), and there was little research on the detection performance of AI in thyroid nodules. To explore the value of a real-time AI based on computer-aided diagnosis system in the detection of thyroid nodules and to analyze the factors influencing the detection accuracy. From June 1, 2022 to December 31, 2023, 224 consecutive patients with 587 thyroid nodules were prospective collected. Based on the detection results determined by two experienced radiologists (both with more than 15 years experience in thyroid diagnosis), the detection ability of thyroid nodules of radiologists with different experience levels (junior radiologist with 1 year experience and senior radiologist with 5 years experience in thyroid diagnosis) and real-time AI were compared. According to the logistic regression analysis, the factors influencing the real-time AI detection of thyroid nodules were analyzed. The detection rate of thyroid nodules by real-time AI was significantly higher than that of junior radiologist (P = 0.013), but lower than that of senior radiologist (P = 0.001). Multivariate logistic regression analysis showed that nodules size, superior pole, outside (near carotid artery), close to vessel, echogenicity (isoechoic, hyperechoic, mixed-echoic), morphology (not very regular, irregular), margin (unclear), ACR TI-RADS category 4 and 5 were significant independent influencing factors (all P < 0.05). With the combination of real-time AI and radiologists, junior and senior radiologist increased the detection rate to 97.4% (P < 0.001) and 99.1% (P = 0.015) respectively. The real-time AI has good performance in thyroid nodule detection and can be a good auxiliary tool in the clinical work of radiologists.

Artificial Intelligence in the Eyes of Society: Assessing Social Risk and Social Value Perception in a Novel Classification

Artificial intelligence (AI) is a rapidly developing technology that has the potential to create previously unimaginable chances for our societies. Still, the public’s opinion of AI remains mixed. Since AI has been integrated into many facets of daily life, it is critical to understand how people perceive these systems. The present work investigated the perceived social risk and social value of AI. In a preliminary study, AI’s social risk and social value were first operationalized and explored by adopting a correlational approach. Results highlighted that perceived social value and social risk represent two significant and antagonistic dimensions driving the perception of AI: the higher the perceived risk, the lower the social value attributed to AI. The main study considered pretested AI applications in different domains to develop a classification of AI applications based on perceived social risk and social value. A cluster analysis revealed that in the two-dimensional social risk × social value space, the considered AI technologies grouped into six clusters, with the AI applications related to medical care (e.g., assisted surgery) unexpectedly perceived as the riskiest ones. Understanding people’s perceptions of AI can guide researchers, developers, and policymakers in adopting an anthropocentric approach when designing future AI technologies to prioritize human well-being and ensure AI’s responsible and ethical development in the years to come.

Healthcare Systems and Artificial Intelligence: Focus on Challenges and the International Regulatory Framework.

Nowadays, healthcare systems are coping with the challenge of countering the exponential growth of healthcarecosts worldwide, to support sustainability and to guarantee access to treatment for all patients. Artificial Intelligence (AI) is the technology able to perform human cognitive functions through the creation ofalgorithms. The value of AI in healthcare and its ability to address healthcare delivery issues has been a subject ofdiscussion within the scientific community for several years. The aim of this work is to provide an overview of the primary uses of AI in the healthcare system, to discuss itsdesirable future uses while shedding light on the major issues related to implications within international regulatoryprocesses. In this manuscript, it will be described the main applications of AI in various aspects of health care, fromclinical studies to ethical implications, focusing on the international regulatory framework in countries in which AI isused, to discuss and compare strengthens and weaknesses. The challenges in regulatory processes to facilitate the integration of AI in healthcare are significant. However,overcoming them is essential to ensure that AI-based technologies are adopted safely and effectively.

AI pricing algorithms under platform competition

AbstractPlatforms play an essential role in the modern economy. At the same time, due to advances in artificial intelligence (AI), algorithms are becoming more widely used for pricing and other business functions. Previous literature examined algorithmic pricing, but not in the context of network effects and platforms. Moreover, platform competition literature has not considered how algorithms may affect competition. We study the performance of AI pricing algorithms (Q-learning and Particle Swarm Optimization) and naïve algorithms (price-matching) under platform competition. We find that algorithms set an optimal price structure that internalizes network effects. However, no algorithm is always the best because profitability depends on the type of competing algorithms and market characteristics, such as differentiation and network effects. Additionally, algorithms learn autonomously when an equilibrium is unstable and avoid it. When algorithm adoption is an endogenous strategic decision, several algorithms can be adopted in equilibrium; we characterize the conditions for the various outcomes and show that the equilibrium and platform profits are sensitive to algorithm design changes. Overall, our research suggests that AI algorithms can be effective in the presence of network effects, and platforms are likely to adopt a variety of algorithms. Lastly, we reflect on the business value of AI and identify opportunities for future research at the intersection of AI algorithms and platforms.

Artificial intelligence in neurology: opportunities, challenges, and policy implications.

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

Exploring the Impact of Artificial Intelligence Tools in Engineering Pedagogy: A Qualitative Survey of Academic Experiences

Abstract: Artificial Intelligence (AI) is revolutionizing the landscape of Engineering Education, albeit with limited empirical qualitative assessment of its potential and perceived benefits or challenges. This paper investigates the integration and experiences of academic professionals with AI tools in their pedagogical practices. The study employs a comprehensive survey asking specific questions relating to the familiarity, motivation, usage, training, benefits, challenges, ethical considerations, and future implications of AI tools like ChatGPT, Google Bard, and Microsoft Bing. The results illustrate a broad understanding and adoption of AI tools, and their instrumental role in enhancing educational methods and pedagogical approaches. However, the findings also highlight the need for adequate training and ethical guidelines for responsible AI use. Additionally, perceived challenges are revealed, underscoring the importance of addressing them to harness AI's full potential in education. Overall, this research contributes to the understanding of AI's value in education from an academic standpoint, outlining its role, advantages, challenges, and future expectations. This study is essential in the ongoing discourse of AI in education, and it can inform the development of training, policies, and strategies for effective and responsible AI integration in the future of engineering education.

Exploring the Impact of Artificial Intelligence Tools in Engineering Pedagogy: A Qualitative Survey of Academic Experiences

Abstract: Artificial Intelligence (AI) is revolutionizing the landscape of Engineering Education, albeit with limited empirical qualitative assessment of its potential and perceived benefits or challenges. This paper investigates the integration and experiences of academic professionals with AI tools in their pedagogical practices. The study employs a comprehensive survey asking specific questions relating to the familiarity, motivation, usage, training, benefits, challenges, ethical considerations, and future implications of AI tools like ChatGPT, Google Bard, and Microsoft Bing. The results illustrate a broad understanding and adoption of AI tools, and their instrumental role in enhancing educational methods and pedagogical approaches. However, the findings also highlight the need for adequate training and ethical guidelines for responsible AI use. Additionally, perceived challenges are revealed, underscoring the importance of addressing them to harness AI's full potential in education. Overall, this research contributes to the understanding of AI's value in education from an academic standpoint, outlining its role, advantages, challenges, and future expectations. This study is essential in the ongoing discourse of AI in education, and it can inform the development of training, policies, and strategies for effective and responsible AI integration in the future of engineering education.

Integrated Diagnostics of Thyroid Nodules.

Thyroid nodules are common findings, particularly in iodine-deficient regions. Our paper aims to revise different diagnostic tools available in clinical thyroidology and propose their rational integration. We will elaborate on the pros and cons of thyroid ultrasound (US) and its scoring systems, thyroid scintigraphy, fine-needle aspiration cytology (FNAC), molecular imaging, and artificial intelligence (AI). Ultrasonographic scoring systems can help differentiate between benign and malignant nodules. Depending on the constellation or number of suspicious ultrasound features, a FNAC is recommended. However, hyperfunctioning thyroid nodules are presumed to exclude malignancy with a very high negative predictive value (NPV). Particularly in regions where iodine supply is low, most hyperfunctioning thyroid nodules are seen in patients with normal thyroid-stimulating hormone (TSH) levels. Thyroid scintigraphy is essential for the detection of these nodules. Among non-toxic thyroid nodules, a careful application of US risk stratification systems is pivotal to exclude inappropriate FNAC and guide the procedure on suspicious ones. However, almost one-third of cytology examinations are rendered as indeterminate, requiring "diagnostic surgery" to provide a definitive diagnosis. 99mTc-methoxy-isobutyl-isonitrile ([99mTc]Tc-MIBI) and [18F]fluoro-deoxy-glucose ([18F]FDG) molecular imaging can spare those patients from unnecessary surgeries. The clinical value of AI in the evaluation of thyroid nodules needs to be determined.

Integrating Artificial Intelligence in Public Relations and Media: A Bibliometric Analysis of Emerging Trends and Influences

Integrating artificial intelligence (AI) techniques in public relations and media is an emerging interdisciplinary research domain warranting greater attention. This study presents the first bibliometric analysis of recent literature at the nexus of AI, public relations, and media. Publications from 2018-2023 were retrieved from Scopus and analyzed to uncover productivity, impact, collaborations, and topics. Results showed rising annual outputs with over 2000 articles published in 2021, confirming intensifying research activity. Recent publications also demonstrated higher citation impact, indicating their contemporary influence. Prolific authors were predominantly China-based, while the US led overall production. China, Western nations, and India dominated but opportunities exist to improve geographic diversity. Initial activity focused on justifying AI's value, evolving to technical applications for social media analytics, predictive modeling, and content creation. International collaborations centered around Western regions, though China's partnerships increased. This quantitative intelligence provides a benchmark to inform future work in this high-potential domain. Bibliometric monitoring should continue as the discourse progresses. Broader participation from underrepresented stakeholders is needed to responsibly shape AI integration in public relations and media.

Edge Computing and AI: Advancements in Industry 5.0- An Experimental Assessment

This empirical research evaluated, via experimentation, how Edge Computing and Artificial Intelligence (AI) work together in the context of Industry 5.0. With a high satisfaction rating of 88%, participants in the Edge Computing condition saw an astonishing 18% decrease in task completion times. Similarly, in the AI integration scenario, participants rated AI's value at 86%, and they saw a significant 12% reduction in task completion times and a noteworthy 7% drop in mistake rates. Significantly, with an astounding 21% gain in work completion times, the Edge Computing and AI combo had the largest performance boost. These results highlight how Edge Computing and AI may dramatically improve industrial efficiency and performance in the context of Industry 5.0, providing insightful information for businesses looking to use these technologies to streamline processes and spur innovation.

Advancements in artificial intelligence for pelvic floor ultrasound analysis.

Pelvic floor ultrasound provides a clear depiction of pelvic floor structures and their spatial anatomical relationships, enabling enhanced observation of pelvic organ function and position. The integration of artificial intelligence (AI) into medical imaging has revolutionized the automatic analysis of imaging data, offering efficient and accurate preprocessing and analysis. This technological advance addresses challenges associated with traditional pelvic floor ultrasound, such as reliance on operator's experience, time-intensive manual measurements, and significant potential for human error. Current AI applications in pelvic floor ultrasound encompass automatic measurement of the angle of progress (AOP), automatic segmentation of the levator hiatus (LH), and automatic identification of the levator ani muscle (LAM). AI excels in mimicking human analysis, distilling patterns from reorganized data. This paper, grounded in a comprehensive literature review, outlines the principal aspects of pelvic floor ultrasound and its augmentation through AI, highlighting the application value and progress of AI in this field.

Recent Progress on the Convergence of the Internet of Things and Artificial Intelligence

Abstract: Artificial Intelligence of Things (AIoT) is the natural growth for both Artificial Intelligence (AI) and Internet of Things (IoT) as they are mutually gainful.. AI raise the value of the IoT through Machine Learning by transforming the data into useful information, although the IoT increases the value of AI through connectivity and data exchange. Hence, InSecTT – Intelligent Secure Trustable Things, a pan-European effort with 52 key partners from 12 countries (EU and Turkey), gives intelligent, secure and trustworthy systems for industrial purposes. This results in global cost-efficient solutions of intelligent, end-to-end secure, authentic connectivity and interoperability to bring the Internet of Things and Artificial Intelligence in sync. InSecTT targets at creating trust in AI-based intelligent systems and solutions as a major part of the AIoT. This paper provides an overview regarding the concept and ideas behind InSecTT and introduces the InSecTT Reference Architecture for infrastructure organization of AIoT use cases.