Development Of Artificial Intelligence Research Articles

Evaluation of top tourism destinations according to their AI development with the entropy-TOPSIS-integrated method

PurposeThis study aims to determine and evaluate the artificial intelligence (AI) development and competitiveness of the top 20 countries that receive the highest number of tourists with the entropy technique for order of preference by similarity to the ideal solution (TOPSIS)-integrated method.Design/methodology/approachThis study is based on Global AI Index data published by Tortoise Media. Based on this index, according to the World Tourism Organization (UNWTO) report, the top 20 destinations that will host the highest number of tourists in 2022 were evaluated in seven different subpillars, which are talent, infrastructure, operating environment, research, development, government strategy and commercial. These seven subpillars of the index were considered as criteria, and the top 20 tourist destinations were included in the research as decision alternatives.FindingsThe analysis results show that the three most important AI criteria are operating environment, infrastructure and government strategy. Furthermore, the first three countries with the best AI performance according to the weighted criteria were the USA, China and the UK, respectively.Practical implicationsConsidering that AI technologies will direct tourist behavior in a world where technology is rapidly developing, it is recommended that the countries that receive the highest number of tourists improve their AI performance.Originality/valueWhen the relevant literature is examined, there is a limited number of studies examining the AI development and competitiveness of the top tourist destinations and weighting the Global AI Index values. Therefore, this study contributes to the gap in the relevant literature.

Insulator defect detection in transmission line based on an improved lightweight YOLOv5s algorithm

As one of the most important components on the transmission line, the insulator is prone to failure, which affects the safe operation of electrical power system. Hence, it is crucial to accurately detect the insulator defects for further maintenance in time. Recently, with the development of artificial intelligence and target detection algorithms, the insulator defect detection has received more and more attention. However, there are still existing some difficulties: insufficient samples and low detection accuracy. To improve the accuracy of insulator defect detection, this paper proposes an auto-detection method based on an improved lightweight YOLOv5s model. First, this paper introduces the basic network frame of YOLOv5s and proposes an improved algorithm by utilizing the GIoU loss function, Mish activation function, and CBAM module. Then, performs data enhancement in the insulator dataset to enhance the robustness of the model. Finally, trains and tests the improved YOLOv5s model, and compares it with traditional target detection algorithms. Compared with traditional target detection algorithms, the AP value of the proposed algorithm in detecting insulator defects can be improved by 5 %. The results demonstrate the improved algorithm proposed in this paper can effectively identify and position the insulator defects.

Artificial intelligence in detection of small bowel lesions and their bleeding risk: A new step forward

The present letter to the editor is related to the study with the title “Automatic detection of small bowel (SB) lesions with different bleeding risk based on deep learning models”. Capsule endoscopy (CE) is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate. The development of artificial intelligence (AI) in CE could simplify physicians’ tasks. The novel deep learning model by Zhang et al seems to be able to identify various SB lesions and their bleeding risk, and it could pave the way to next perspective studies to better enhance the diagnostic support of AI in the detection of different types of SB lesions in clinical practice.

Artificial Intelligence in Medicine: A New Frontier

Artificial intelligence (AI) refers to the engineering and science of making intelligent machines through algorithms or rules, mimicking human cognitive functions, such as learning and problem-solving. AI has several branches, such as machine learning and deep learning, which can add intelligence to applications. Machine learning is the study of algorithms that allow computer programs to improve automatically through experience. Deep learning algorithms learn from an extensive, multi-layered collection of interconnected processes and expose these processors to many examples. In the coming years, the integration of AI in routine medical care is expected to revolutionize Medicine, potentially improving patient care and quality of life. The time required for a diagnosis can be greatly reduced, and diagnostic efficiency can be significantly enhanced when AI assists clinicians. Large language model chatbots are capable of clinical expert-level medical note-taking, consultation, and questionanswering. Chatbotscan generate human-like text, may help diagnose diseases based on medical records, and may suggest treatment options or plans. Artificial intelligence algorithms, particularly deep learning, have demonstrated remarkable progress in radiological image analysis and diagnosis and may improve radiologists’ efficiency. These algorithms may also improve diagnostic accuracy in dermatology, histopathology, fundoscopy, endoscopy, and other medical images. Natural language processing and ambient clinical intelligence automate administrative duties like recording patient visits in electronic health records, streamlining clinical workflow, and freeing up doctors to spend more time with patients. AI may also help with new drug discoveries, precision medicine, and clinical research. AI developments can revolutionize several healthcare-related fields and pave the way for a more individualized, accurate, predictive, and portable future. Bangladesh J Medicine 2024; 35: 54-60

Computational Legal Studies Comes of Age

Computational analysis techniques are transforming empirical legal scholarship. Two paradigms have emerged: law-as-code, which seeks to represent legal rules in a logical, executable format; and law-as-data, which leverages quantitative analysis of legal texts to reveal patterns and insights. This article surveys these approaches, emphasizing recent developments in large language models and generative artificial intelligence (AI). Law-as-code systems have enabled applications from tax preparation software to smart contracts, but realizing the vision of fully computational law has proven challenging. Law-as-data techniques like natural language processing and machine learning have charted the semantic relationship between courts and illuminated changes in judicial culture. Generative models showcase AI's explosive progress, with impressive feats like passing the U.S. bar example, but they also highlight limitations like factual inaccuracy and interpretability issues. Hybrid approaches integrating computational law, data science, and AI offer a promising research direction. As these tools spread, legal scholars can analyze more legal data than ever before, but they must remain cognizant of challenges like biased or low-quality data and linguistic/cultural limitations. Used judiciously alongside traditional methods, computational analysis has the potential to revolutionize empirical legal studies.

The Race of Artificial Intelligence and Challenges

In today’s era Artificial Intelligence (AI) has become an increasingly prominent field of research and development over the past few decades. In recent years, the development of AI has been accelerating, with more and more resources being devoted to the creation of increasingly sophisticated and capable AI systems. With more and more countries being in an AI arm race, companies competing and racing to be the one with the next most disruptive and transformative AI system. Artificial Intelligence (AI) is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to limited itself to procedures that are biologically observable. While no exact definition of Artificial Intelligence (AI) exists, AI is broadly characterized as the study of computations that allow for reason, perception and action. Today, the amount of data that is generated, by both humans and machines, far outpaces humans’ ability to absorb, interpret, and make complex decisions based on that data. Artificial intelligence forms the basis for all computer learning and is the future of all complex decision making. This paper focuses features of artificial Intelligence, introduction, history, applications, growth and achievements. This paper also analyzes the current state of artificial intelligence progresses, and how the current AI race with recent trends.

The impact of artificial intelligence big data technology on the development of media economy information security

With the rapid development of Artificial Intelligence (AI) and Big Data (BD), they provide people with new ways and tools in information acquisition, processing and dissemination. The purpose of this study is to deeply discuss the influence of AI and BD technologies on the development of Information Security (InfoSec) in media economy, and analyze the opportunities and challenges it brings. Firstly, the evaluation system of the influence of BD technology on the development of InfoSec, a media enterprise, is constructed. Then, this study discusses the development mode of the multilateral platform of media economy supported by BD technology. Finally, taking S company and X platform as examples, this study analyses the influence of BD technology on the development of InfoSec and the development mode of media economy supported by BD technology. The research results show that with the support of BD technology, the proportion of InfoSec products in enterprises has reached 0.2426, and the InfoSec score of S company has reached 77.74, reaching a high level. The profit margin of digital network media is three times that of traditional media. The number of users of multilateral digital media economic platform X has increased by 21%, and its turnover has increased by 54%. AI and BD technologies have brought opportunities and challenges to the InfoSec development of media economy. While applying these technologies, it is necessary to take effective measures to protect users’ privacy and strengthen the information review and verification mechanism to ensure the sustainable development of media economy. The uniqueness of this study lies in the construction of a comprehensive BD technology impact assessment system and the discussion of the development model of multilateral media economic platform, which provides a powerful reference and guidance for media enterprises. In addition, this study also reveals the positive influence of BD technology on InfoSec and the development of media economy, which provides beneficial enlightenment for the future development of media industry. In response to the challenges of privacy and InfoSec, this study puts forward important policy and practical suggestions to ensure the sustainable growth of media economy and the protection of users’ rights and interests. Therefore, this study has important contribution and innovative value in both theory and practice.

Integrating Data and Model Optimization for Improved Multi-Class Land Cover Classification Using Multispectral Remote Sensing Data

Abstract. With the rapid development of artificial intelligence, significant progress has been made in land cover classification using deep learning methods. However, in existing research, most studies focus more on improving classification accuracy by optimizing the model structure and less on mining the value of the data itself. In this paper, experiments on remote sensing multi-class land cover classification were conducted based on Worldview3 data, and strategies to improve classification accuracy were proposed in terms of sampling methods, band combination, loss function, and model optimization. Experiment results show that the proposed improvement strategies are effective for multi-class land cover classification, with recall, F1, and IoU improved by 29%, 17%, and 19%, respectively. The significant improvement in classification accuracy for less-represented targets confirms that enhancing data richness and balance leads to greater improvement than just optimizing the model.

Indonesia-Malaysia Batik Pattern Collaboration Creation with Artificial Intelligence Platform

The problem with making batik in Indonesia and Malaysia so far is that it is still very manual and has not taken much advantage of technological advances. The development of Artificial Intelligence (AI) technology is currently very advanced, continues to innovate, and has even touched various aspects of human life. Limitations in ideas, human resources, knowledge, tools, and internet networks are the dominant conditions that cause problems in Indonesian and Malaysian batik creations. This study aims to use Artificial Intelligence to describe the combination of Indonesian and Malaysian batik patterns. This paper uses a descriptive-qualitative method with an experimental approach in the form of trials for the creation of batik patterns using the Imagine Artificial Intelligence platform; each trial is based on three basic patterns of batik, namely Sawat and Gurdha (Indonesia) and Orchid and Tiger (Malaysia). The results of this paper generated eight totally new Indonesian-Malaysian batik patterns. Artificial Intelligence (AI) was very helpful for the development of batik patterns. Through the creation of new batik patterns with the support of Artificial Intelligence (AI), it certainly creates new opportunities for the creation and business of batik to become more advanced. It is hoped that this paper will succeed in solving the problem of limited ideas in creating Indonesian-Malaysian batik motifs. Keywords: Artificial Intelligence, batik, creation, Indonesia-Malaysia

Towards Transnational Fairness in Machine Learning: A Case Study in Disaster Response Systems

Research on fairness in machine learning (ML) has been largely focusing on individual and group fairness. With the adoption of ML-based technologies as assistive technology in complex societal transformations or crisis situations on a global scale these existing definitions fail to account for algorithmic fairness transnationally. We propose to complement existing perspectives on algorithmic fairness with a notion of transnational algorithmic fairness and take first steps towards an analytical framework. We exemplify the relevance of a transnational fairness assessment in a case study on a disaster response system using images from online social media. In the presented case, ML systems are used as a support tool in categorizing and classifying images from social media after a disaster event as an almost instantly available source of information for coordinating disaster response. We present an empirical analysis assessing the transnational fairness of the application’s outputs-based on national socio-demographic development indicators as potentially discriminatory attributes. In doing so, the paper combines interdisciplinary perspectives from data analytics, ML, digital media studies and media sociology in order to address fairness beyond the technical system. The case study investigated reflects an embedded perspective of peoples’ everyday media use and social media platforms as the producers of sociality and processing data-with relevance far beyond the case of algorithmic fairness in disaster scenarios. Especially in light of the concentration of artificial intelligence (AI) development in the Global North and a perceived hegemonic constellation, we argue that transnational fairness offers a perspective on global injustices in relation to AI development and application that has the potential to substantiate discussions by identifying gaps in data and technology. These analyses ultimately will enable researchers and policy makers to derive actionable insights that could alleviate existing problems with fair use of AI technology and mitigate risks associated with future developments.

Cybersecurity Issue in the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence from October 30, 2023

Cybersecurity Issue in the Executive Order on the Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence from October 30, 2023

Micro-Scale Particle Tracking: From Conventional to Data-Driven Methods

Micro-scale positioning techniques have become essential in numerous engineering systems. In the field of fluid mechanics, particle tracking velocimetry (PTV) stands out as a key method for tracking individual particles and reconstructing flow fields. Here, we present an overview of the micro-scale particle tracking methodologies that are predominantly employed for particle detection and flow field reconstruction. It covers various methods, including conventional and data-driven techniques. The advanced techniques, which combine developments in microscopy, photography, image processing, computer vision, and artificial intelligence, are making significant strides and will greatly benefit a wide range of scientific and engineering fields.

Anomaly-based Intrusion Detection System in Industrial IoT-Healthcare Environment Network

The Internet of Things (IoT) technology facilitates automation, monitoring, and control of tangible objects and surroundings by enabling connected devices to interact and exchange data over the Internet. Developments in edge computing, blockchain, and artificial intelligence (AI) are incorporated into IoT technologies for more reliable operations. Inadequate authorization, authentication, and encryption protocols could render IoT networks insecure and open the door to illegal access and data breaches which can have terrible consequences, most notably in the healthcare industry. In this regard, to identify malicious and incursion traffic, machine learning (ML) is crucial to Internet of Things (IoT) cybersecurity. The paper proposes a framework to detect intrusion or malicious traffic in IoT-enabled different medical equipment such as medical sensors, and controllers for real-time data collection, creating communication channels and data monitoring and analysis over locally available network nodes. IoT-Flock has been utilized for both normal and malicious traffic generation in a wide dataset found by the sensors connected to IoT integrated healthcare network. The feature selection-based proposed framework has been evaluated by three distinct machine learning classifiers, KNN, RF, and DT where corresponding accuracy, sensitivity, precision, and F1-score have been measured for performance analysis. With an accuracy of 99.74%, the KNN technique performed better than the other tactics used by RF and DT regarding intrusion detection in IoT networks. The suggested framework will be helpful in developing or analyzing security solutions in IoT-integrated network systems.

Advances in determining the gross tumor target volume for radiotherapy of brain metastases

Brain metastases (BMs) are the most prevalent intracranial malignant tumors in adults and are the leading cause of mortality attributed to malignant brain diseases. Radiotherapy (RT) plays a critical role in the treatment of BMs, with local RT techniques such as stereotactic radiosurgery (SRS)/stereotactic body radiotherapy (SBRT) showing remarkable therapeutic effectiveness. The precise determination of gross tumor target volume (GTV) is crucial for ensuring the effectiveness of SRS/SBRT. Multimodal imaging techniques such as CT, MRI, and PET are extensively used for the diagnosis of BMs and GTV determination. With the development of functional imaging and artificial intelligence (AI) technology, there are more innovative ways to determine GTV for BMs, which significantly improve the accuracy and efficiency of the determination. This article provides an overview of the progress in GTV determination for RT in BMs.

The emergence of enhanced intelligence in a brain-inspired cognitive architecture

The Causal Cognitive Architecture is a brain-inspired cognitive architecture developed from the hypothesis that the navigation circuits in the ancestors of mammals duplicated to eventually form the neocortex. Thus, millions of neocortical minicolumns are functionally modeled in the architecture as millions of “navigation maps.” An investigation of a cognitive architecture based on these navigation maps has previously shown that modest changes in the architecture allow the ready emergence of human cognitive abilities such as grounded, full causal decision-making, full analogical reasoning, and near-full compositional language abilities. In this study, additional biologically plausible modest changes to the architecture are considered and show the emergence of super-human planning abilities. The architecture should be considered as a viable alternative pathway toward the development of more advanced artificial intelligence, as well as to give insight into the emergence of natural human intelligence.

Factors influencing the development of artificial intelligence in orthodontics.

Since developing AI procedures demands significant computing resources and time, the implementation of a careful experimental design is essential. The purpose of this study was to investigate factors influencing the development of AI in orthodontics. A total of 162 AI models were developed, with various combinations of sample sizes (170, 340, 679), input variables (40, 80, 160), output variables (38, 76, 154), training sessions (100, 500, 1000), and computer specifications (new vs. old). The TabNet deep-learning algorithm was used to develop these AI models, and leave-one-out cross-validation was applied in training. The goodness-of-fit of the regression models was compared using the adjusted coefficient of determination values, and the best-fit model was selected accordingly. Multiple linear regression analyses were employed to investigate the relationship between the influencing factors. Increasing the number of training sessions enhanced the effectiveness of the AI models. The best-fit regression model for predicting the computational time of AI, which included logarithmic transformation of time, sample size, and training session variables, demonstrated an adjusted coefficient of determination of 0.99. The study results show that estimating the time required for AI development may be possible using logarithmic transformations of time, sample size, and training session variables, followed by applying coefficients estimated through several pilot studies with reduced sample sizes and reduced training sessions.

Optimizing Higher Education Using Artificial Intelligence

A revolutionary transformation and overhaul of our approach to how we instruct and interact with educational content is being heralded by the simultaneous development of artificial intelligence and education. Businesses dealing with educational technology are greatly engaging and spending exponentially over technologies delivering education by artificial intelligence. Such technologies generate highly interactive and deeply engaging educational experiences for the learners creating an everlasting impact. Some of the cutting-edge technologies that improve student engagement and encourage deeper connections with the educational content or the conversational assistants that interact verbally and virtually. Virtual reality (VR), Augmented reality (AR), holographic simulations, etc. are some more examples of AI generated applications which are now being used to create immersive learning experiences. This paper explores the role of AI in higher education, it’s benefits for both learners & educators, it’s application in H.E. classrooms, utilisation of AI-detection technology, challenges and strategies to implement AI seamlessly in educational settings. It is an effort made in order to discuss this emerging topic and to offer an insight into the constantly evolving relationship between AI an education.

Judicial Judgment Standard of Generative Artificial Intelligence and Training Data from the Perspective of Fair Use

With the development of generative artificial intelligence, text and data mining has been put into more applications. However, as a technology for collecting and processing a large number of other works, it faces copyright infringement under the current legal system. As a step in the operation of artificial intelligence, text and data mining is to collect and process existing works and information to realize the behavior of knowledge "reproduction", thus promoting the utilization of knowledge and social innovation. Meanwhile, copyright exemption should be provided for its fair use system. The fair use system in the United States can give full play to the autonomy of judges in specific cases and flexibly identify fair use exemptions, while the copyright exception in Europe clarifies that text and data mining activities should be exempted under specific circumstances. Learning from both, China can not only break the closed fair use system, but also build an evaluation system of artificial intelligence infringement and benefit-sharing mechanism, so as to provide scientific norms for fair use and improve the development of artificial intelligence and big data industry in China.

Exploring Motivators for Trust in the Dichotomy of Human—AI Trust Dynamics

This study analyses the dimensions of trust in artificial intelligence (AI), focusing on why a significant portion of the UK population demonstrates a higher level of trust in AI compared to humans. Conducted through a mixed-methods approach, this study gathered 894 responses, with 451 meeting the criteria for analysis. It utilised a combination of a six-step Likert-scale survey and open-ended questions to explore the psychological, sociocultural, and technological facets of trust. The analysis was underpinned by structural equation modelling (SEM) and correlation techniques. The results unveil a strong predilection for trusting AI, mainly due to its perceived impartiality and accuracy, which participants likened to conventional computing systems. This preference starkly contrasts with the scepticism towards human reliability, which is influenced by the perception of inherent self-interest and dishonesty in humans, further exacerbated by a general distrust in media narratives. Additionally, this study highlights a significant correlation between distrust in AI and an unwavering confidence in human judgment, illustrating a dichotomy in trust orientations. This investigation illuminates the complex dynamics of trust in the era of digital technology, making a significant contribution to the ongoing discourse on AI’s societal integration and underscoring vital considerations for future AI development and policymaking.

Some Aspects of Artificial Intelligence Development Strategy for Mobile Technologies

Some Aspects of Artificial Intelligence Development Strategy for Mobile Technologies