Principles Of Artificial Intelligence Research Articles

Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development

Several radiology artificial intelligence (AI) courses are offered by a variety of institutions and educators. The major radiology societies have developed AI curricula focused on basic AI principles and practices. However, a specific AI curriculum focused on pediatric radiology is needed to offer targeted education material on AI model development and performance evaluation. There are inherent differences between pediatric and adult practice patterns, which may hinder the application of adult AI models in pediatric cohorts. Such differences include the different imaging modality utilization, imaging acquisition parameters, lower radiation doses, the rapid growth of children and changes in their body composition, and the presence of unique pathologies and diseases, which differ in prevalence from adults. Thus, to enhance radiologists’ knowledge of the applications of AI models in pediatric patients, curricula should be structured keeping in mind the unique pediatric setting and its challenges, along with methods to overcome these challenges, and pediatric-specific data governance and ethical considerations. In this report, the authors highlight the salient aspects of pediatric radiology that are necessary for AI education in the pediatric setting, including the challenges for research investigation and clinical implementation.

Study on the Teaching Model of Artificial Intelligence Principles Course Based on Discipline Integration and Industry-Education Integration

Artificial intelligence is one of the most promising and influential fields of science and technology in the current era. How to cultivate high-quality talents with artificial intelligence literacy and ability is an important issue faced by the education sector. This paper designs a teaching model of artificial intelligence principles course based on discipline integration and industry-education integration, and introduces the specific content of this teaching model from two aspects: theoretical teaching and practical teaching. The teaching practice effect is evaluated and feedback is given. The results show that this teaching model helps to improve students’ understanding, interest, practice and innovation of artificial intelligence. This paper has certain reference value and enlightenment for promoting the development and reform of artificial intelligence education.

Toward a better understanding of coastal salt marsh mapping: A case from China using dual-temporal images

Coastal salt marshes suffering from anthropogenic coastal development and sea level rise have attracted much attention because of their capacity for carbon sequestration and global climate change mitigation. Accurate mapping of coastal salt marshes is always the first step for their protection, management, and restoration. The inherent complexities of vegetation, dynamics of tides, and anthropogenic disturbances pose challenges for remote sensing-based approaches. Existing studies have utilized phenology information and various black-box algorithms to reduce misclassifications. However, the approaches with dual-temporal images containing phenology information have suffered from inefficiency; the misclassifications have been objectively post-processed rather than considered in the method design, and the tacit knowledge of the trained black-box models has not been revealed. To address the above issues, we proposed a new approach with solid improvements in dual-temporal image construction, misclassification processing, and tacit knowledge analysis, including an efficient method to synthesize dual-temporal images based on the common divisor of the green-up season or senescence season resulting from latitudinal gradients in narrow coastal areas of China, a detailed classification scheme by interpretation of iteratively collected samples, and a key decision rule approximating the trained model. We applied the approach to Sentinel-1/2 images and DEM data, thus deriving a 10-m resolution coastal salt marsh map of China with an overall accuracy of 92.5%. A qualitative comparison showed that the map produced in this study was fitted well with actual salt marsh distributions, resulting in improved accuracy when compared to recently generated maps. The most important contribution is that the overall nature of the trained model observed from the training samples was approximated by a four-feature decision rule following the principle of explainable artificial intelligence, i.e., B8senescence/B4senescence < 2.06 & B4green/B8green < 0.78 & B12green/B11green < 0.72 & elevation < 2.13, and thus established a new potential classification mechanism for dual-temporal black-box algorithms, i.e., the water signal was covered up by flourishing vegetation, but the situation changed when vegetation withered. This study not only generates an accurate coastal salt marsh map at a national scale but also provides a classification mechanism for dual-temporal image-based coastal salt marsh identification and mapping.

Deep Learning based Breast Cancer Diagnostic System using Medical Images

A common and lethal kind of cancer, breast cancer, affects women worldwide. In the year 2020, around 2.26 million breast cancer cases were reported worldwide. In 2020, breast cancer will become the most common cancer globally with a projected 11.7% of all cancer cases or 2.3 million new cases. It is ranked as 7th cancer cause globally with 685,000 deaths. Diagnosis plays an essential role in cancer, since early diagnosis of the condition can help in better planning for treatment and prevent further complications. This research develops an integrated system to aid oncologists and clinicians in the diagnosis, confirmation and follow-up analysis for breast cancer using principles of artificial intelligence and medical imaging modalities. The decision making is made by deep learning models trained on thousands of images of several medical imaging modalities. On the whole, the proposed system can help the clinicians in their medical decisions and provide better service for patients with breast cancer.

Neural Network Applications in Electrical Drives—Trends in Control, Estimation, Diagnostics, and Construction

Currently, applications of the algorithms based on artificial intelligence (AI) principles can be observed in various fields. This can be also noticed in the wide area of electrical drives. Consideration has been limited to neural networks; however, the tasks for the models can be defined as follows: control, state variable estimation, and diagnostics. In the subsequent sections of this paper, electrical machines, as well as power electronic devices, are assumed as the main objects. This paper describes the basics, issues, and possibilities related to the used tools and explains the growing popularity of neural network applications in automatic systems with electrical drives. The paper begins with the overall considerations; following that, the content proceeds with the details, and two specific examples are shown. The first example deals with a neural network-based speed controller tested in a structure with a synchronous reluctance motor. Then, the implementation of recurrent neural networks as state variable estimators is analyzed. The achieved results present a precise estimation of the load speed and the shaft torque signals from a two-mass system. All descriptions in the article are considered in the context of the trends and perspectives in modern algorithm applications for electrical drives.

AI-Powered Personalized Mobile Education for New Zealand Students

This research endeavors to develop and assess a customized mobile education system for students in New Zealand, employing the principles of artificial intelligence (AI) and user-centered design (UCD). The objective is to overcome the limited personalization observed in current mobile education solutions by offering tailored learning content and recommendations based on individual preferences, thereby accommodating the diverse requirements of students. A mixed-methods approach will be utilized, encompassing user research, persona development, user journey mapping, design, development, and evaluation. Participants, including New Zealand students, parents, and teachers, will actively engage in multiple research phases to ensure the effective implementation of user-centered design principles. By showcasing the potential of AI-driven personalization in enhancing the learning experience for students, this study contributes to the growing utilization of AI algorithms and systems within the educational context.

Deploying artificial intelligence for anti-money laundering and asset recovery: the dawn of a new era

PurposeThis paper aims to critically examine the digital transformation of anti-money laundering (AML) and countering the financing of terrorism (CFT) in light of the Financial Action Task Force (FATF) San Jose principles, the Organisation for Economic Co-operation and Development (OECD) principles for artificial intelligence (AI) and the proposed European Union (EU) Artificial Intelligence Act. The authors argue that AI tools can revolutionize AML/CFT and asset recovery, but there is a need to strike a balance between optimizing AML efficiency and safeguarding fundamental rights.Design/methodology/approachThis paper draws on reports, legislation, legal scholarships and other open-source data on the digital transformation of AML/CFT, particularly the deployment of AI in this context.FindingsA new regulatory framework with robust safeguards is necessary to mitigate the risks associated with the use of new technologies in the AML context.Originality/valueThis study is one of the first to examine the use of AI in the AML/CFT context in light of the FATF San Jose principles, the OECD AI principles and the proposed EU AI Act.

Safeguarding Connected Health: Leveraging Trustworthy AI Techniques to Harden Intrusion Detection Systems Against Data Poisoning Threats in IoMT Environments

Internet of Medical Things (IoMT) environments introduce vast security exposures including vulnerabilities to data poisoning threats that undermine integrity of automated patient health analytics like diagnosis models. This research explores applying trustworthy artificial intelligence (AI) methodologies including explainability, bias mitigation, and adversarial sample detection to substantially enhance resilience of medical intrusion detection systems. We architect an integrated anomaly detector featuring purpose-built modules for model interpretability, bias quantification, and advanced malicious input recognition alongside conventional classifier pipelines. Additional infrastructure provides full-lifecycle accountability via independent auditing. Our experimental intrusion detection system design embodying multiple trustworthy AI principles is rigorously evaluated against staged electronic record poisoning attacks emulating realistic threats to healthcare IoMT ecosystems spanning wearables, edge devices, and hospital information systems. Results demonstrate significantly strengthened threat response capabilities versus baseline detectors lacking safeguards. Explainability mechanisms build justified trust in model behaviors by surfacing rationale for each prediction to human operators. Continuous bias tracking enables preemptively identifying and mitigating unfair performance gaps before they widen into operational exposures over time. SafeML classifiers reliably detect even camouflaged data manipulation attempts with 97% accuracy. Together the integrated modules restore classification performance to baseline levels even when overwhelmed with 30% contaminated data across all samples. Findings strongly motivate prioritizing adoption of ethical ML practices to fulfill duty of care around patient safety and data integrity as algorithmic capabilities advance.

Guidance for researchers and peer-reviewers on the ethical use of Large Language Models (LLMs) in scientific research workflows

For researchers interested in exploring the exciting applications of Large Language Models (LLMs) in their scientific investigations, there is currently limited guidance and few norms for them to consult. Similarly, those providing peer-reviews on research articles where LLMs were used are without conventions or standards to apply or guidelines to follow. This situation is understandable given the rapid and recent development of LLMs that are capable of valuable contributions to research workflows (such as OpenAI’s ChatGPT). Nevertheless, now is the time to begin the development of norms, conventions, and standards that can be applied by researchers and peer-reviewers. By applying the principles of Artificial Intelligence (AI) ethics, we can better ensure that the use of LLMs in scientific research aligns with ethical principles and best practices. This editorial hopes to inspire further dialogue and research in this crucial area of scientific investigation.

Development of Mobile Application in Assessing Commuting Accident Risk (CommuRisk) Amongst Commuters at Klang Valley

Introduction: Globally, commuting accident risks are always neglected in an organisation. There is a need to assess the impact of commuting accidents based on sociodemographic, human, vehicle, road, and environmental factors and to find suitable and effective mitigation strategies to alleviate the associated undesirable outcomes. Methods: This research was designed to develop a mobile application to assess commuting accident risk levels using artificial intelligence principles, as we are now in the 21st-century technology era. A total of 216 respondents from private and government industries participated in this study. Besides, to prove the developed application’s effectiveness, the study evaluated the effectiveness of the identified risk factor in determining the level of commuting risks predicted by respondents with the risk level calculated by the mobile application. Results: A major contribution of this paper is the effectiveness and accuracy of a mobile application known as CommuRisk. The app was developed using Android Studio and natively uses Java. There was a significant difference between with and without mobile applications in determining the level of commuting risks, and the effectiveness was proven with a (p-value = 0.001) at a 95% confidence interval with large sample size. Conclusion: Thus, this paper proved the effectiveness and accuracy of a mobile application in calculating risk levels exposed by commuters compared to risk levels predicted by commuters.

Cooperative Spectrum Sensing in Cognitive Radio Networks: A Systematic Review

Background:Spectrum is the backbone for wireless communications including internet services. Now days, the business of industries providing wired communication is constant while the business of industries dealing with wireless communications is growing very fast. There is large demand of radio spectrum for new wireless multimedia services. Although the present fixed spectrum allotment schemes do not cause any interference between users, but this fixed scheme of spectrum allocation do not allow accommodating the spectrum required for new wireless services. Cognitive radio (CR) relies on spectrum sensing to discover available frequency bands so that the spectrum can be used to its full potential, thus avoiding interference to the primary users (PU).Objectives:The purpose of this work is to present an in-depth overview of traditional as well as advanced artificial intelligence and machine learning based cooperative spectrum sensing (CSS) in cognitive radio networks.Method:Using the principles of artificial intelligence (AI), systems are able to solve issues by mimicking the function of human brains. Moreover, since its inception, machine learning has demonstrated that it is capable of solving a wide range of computational issues. Recent advancements in artificial intelligence techniques and machine learning (ML) have made it an emergent technology in spectrum sensing.Result:The result shows that more than 80% papers are on traditional spectrum sensing while less than 20% deals with artificial intelligence and machine learning approaches. More than 75% papers address the limitation of local spectrum sensing. The study presents the various methods implemented in the spectrum sensing along with merits and challenges.Conclusion:Spectrum sensing techniques are hampered by a variety of issues, including fading, shadowing, and receiver unpredictability. Challenges, benefits, drawbacks, and scope of cooperative sensing are examined and summarized. With this survey article, academics may clearly know the numerous conventional artificial intelligence and machine learning methodologies used and can connect sharp audiences to contemporary research done in cognitive radio networks, which is now underway.

Investigating Students' Perceptions towards Artificial Intelligence in Medical Education.

Implementing a reform in medical education requires students' awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students' perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait. The response rate for the survey was 51%, with a sample size of 352. Most students (349 (99.1%)) agreed that AI would play an important role in healthcare. More than half of the students (213 (60.5%)) understood the basic principles of AI, and (329 (93.4%)) students showed comfort with AI terminology. Many students (329 (83.5%)) believed that learning about AI would benefit their careers, and (289 (82.1%)) believed that medical students should receive AI teaching or training. The study revealed that most students had positive perceptions of AI. Undoubtedly, the role of AI in the future of medicine will be significant, and AI-based medical practice is required. There was a strong consensus that AI will not replace doctors but will drastically transform healthcare practices.

Evaluating Europe's push to enact AI regulations: how will this influence global norms?

ABSTRACT Artificial intelligence (AI) policy, innovation, and practice are moving ahead in rapid fashion. There is a growing mismatch between technological innovations in AI, which are advancing at a rapid rate, and normative and regulatory frameworks, which are lagging, particularly when it comes to protecting democratic values and human rights principles. National governments and multilateral institutions are attempting to catch up. At least 175 countries, firms and other organizations have produced documents listing ethical principles for AI. These efforts have proceeded in a somewhat fragmented manner, yet there are emerging signs of consolidation as the United States, Europe, and other countries begin to coalesce around shared principles. Europe, in particular, has raced ahead to draft comprehensive legislation, the Artificial Intelligence Act (AIA), to oversee these technologies and systems. What has motivated the European Union to pursue this approach? And how will this effort influence AI norms globally? This paper describes how Europe’s AI norm-building process represents an effort to ensure EU priorities are reflected in the AI governance landscape. Europe’s approach faces uncertainty. While it is likely that the AIA will meaningfully influence global AI norms, several factors may hinder its global diffusion and adoption.

Regulating Artificial Intelligence in International Investment Law

Abstract The interaction between artificial intelligence (AI) and international investment treaties is an uncharted territory of international law. Concerns over the national security, safety, and privacy implications of AI are spurring regulators into action around the world. States have imposed restrictions on data transfer, utilised automated decision-making, mandated algorithmic transparency, and limited market access. This article explores the interaction between AI regulation and standards of investment protection. It is argued that the current framework provides an unpredictable legal environment in which to adjudicate the contested norms and ethics of AI. Treaties should be recalibrated to reinforce their anti-protectionist origins, embed human-centric AI principles, and embrace expert witnesses and amicus briefs.

AI Accountability: Approaches, Affecting Factors, and Challenges

Accountability is one of core ethical principles for artificial intelligence (AI) systems. It is often cited as a must-have property especially in high-stake applications. This survey article investigates AI accountability from different key perspectives for its effective operationalization. Major approaches for achieving accountability are reviewed and typical factors affecting accountability are identified.

Regulating and testing normative structures for Artificial Intelligence: the possibilities of the Regulatory Living Lab

Abstract: This qualitative and exploratory study sought to understand how the Living Lab approach can serve as an innovative alternative to test a regulatory structure organized by principles for Artificial Intelligence (AI). The main research problem is: what would be the structuring elements of a non-legislative regulatory model that could standardize the advances in Artificial Intelligence? The units of analysis were the developments and commercialized innovative AI applications of startups from an incubator that integrates an internationally renowned technological park in Brazil. Entrepreneur perceptions of the dimensions of the Living Lab approach are described, analyzed, and compared (i.e. the interaction between multiple stakeholders, co-creation of value, user-centered research, solving real-world problems, and adopting multiple methodologies). The findings suggest that the Living Lab approach is an alternative for creating and developing innovations and applications in AI, with the active involvement of end-users while facing existing regulations and testing environments for new regulations - principles published by the European Union and OECD. An important observation that emerged from the study: the advances in Artificial Intelligence have occurred on an increasing scale, very close to exponentiality. For this reason, the need and importance of spreading the regulatory importance of these “what should be” principles, is very similar to those of the rules produced by state legislative action. In this exploratory study, it became evident that entrepreneurs feel there is a lack of clear regulatory mechanisms. The role of the Law in the creative proposition of regulatory models should be based on globally accepted principles, such as those published by the European Union and the OECD.

Ethics of Artificial Intelligence in Society

ABSTRACT: Every day, artificial intelligence (AI) is becoming more prevalent as new technologies are presented to the public with the intent of integrating them into society. However, these systems are not perfect and are known to cause failures that impact a multitude of people. The purpose of this study is to explore how ethical guidelines are followed by AI when it is being designed and implemented in society. Three ethics theories, along with nine ethical principles of AI, and the Agent, Deed, Consequence (ADC) model were investigated to analyze failures involving AI. When a system fails to follow the models listed, a set of refined ethical principles are created. By analyzing the failures, an understanding of how similar incidents may be prevented was gained. Additionally, the importance of ethics being a part of AI programming was demonstrated, followed by recommendations for the future incorporation of ethics into AI. The term “failure” is specifically used throughout the paper because of the nature in which the events involving AI occur. The events are not necessarily “accidents” since the AI was intended to act in certain ways, but the events are also not “malfunctions” because the AI examples were not internally compromised. For these reasons, the much broader term “failure” is used. KEYWORDS: Ethics; Artificial Intelligence; Agent-Deed-Consequence (ADC) Model; Principles of Artificial Intelligence; Virtue Ethics; Deontology; Consequentialism; AI Systems

EMPIRICAL STUDIES OF EMERGENCY MODE PARAMETERS AND THEIR INTERACTION IN DETERMINING THE FAULT LOCATION IN NETWORKS WITH AN ISOLATED NEUTRAL VOLTAGE OF 6(10) - 35 KV

"This article considers experimental confirmation of quantitative characteristics of factors influencing the determination of the distance to the fault location in networks with an isolated neutral voltage of 6(10)-35 kV. Experiments were carried out on a 35 kV overhead power line, the selection of optimality criteria was provided, a mathematical model, an experimental plan, a method for analyzing measurement data to damage detection devices were developed, response surfaces were constructed in the Mathcad 14 program in the Pro/ENGINEER package. According to the results of the experiment on the parameters of the emergency mode, it was found out that the largest influencing factor is the value of the transient resistance at the point of wire breakage of a non-metallic earth fault. It is proved that with a transient resistance above the value of 50 ohms, the distance to the damage site is almost impossible to determine with remote devices that register the parameters of the emergency mode. Recommendations for the development of a mathematical model with the production of logical operations on fuzzy sets of an adaptive neuro-fuzzy system are formulated. It is proposed to apply the mathematical model developed in the future as a basis for designing the element base of the device for determining the location of damage, working on the principle of artificial intelligence with an adaptive nature of measurements under changing external conditions. Key words: experiment, single-phase ground fault, fault location, overhead power line, isolated neutral, contact resistance, network isolation. "

Human-Centered Design to Address Biases in Artificial Intelligence

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care.

Ethics Principles for Artificial Intelligence-Based Telemedicine for Public Health.

The use of artificial intelligence (AI) in the field of telemedicine has grown exponentially over the past decade, along with the adoption of AI-based telemedicine to support public health systems. Although AI-based telemedicine can open up novel opportunities for the delivery of clinical health and care and become a strong aid to public health systems worldwide, it also comes with ethical risks that should be detected, prevented, or mitigated for the responsible use of AI-based telemedicine in and for public health. However, despite the current proliferation of AI ethics frameworks, thus far, none have been developed for the design of AI-based telemedicine, especially for the adoption of AI-based telemedicine in and for public health. We aimed to fill this gap by mapping the most relevant AI ethics principles for AI-based telemedicine for public health and by showing the need to revise them via major ethical themes emerging from bioethics, medical ethics, and public health ethics toward the definition of a unified set of 6 AI ethics principles for the implementation of AI-based telemedicine. (Am J Public Health. 2023;113(5):577-584. https://doi.org/10.2105/AJPH.2023.307225).