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The exponential use of artificial intelligence (AI) to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed. While AI is a powerful means to discover interesting patterns and obtain predictive models, the use of these algorithms comes with a great responsibility, as an incomplete or unbalanced set of training data or an unproper interpretation of the models' outcomes could result in misleading conclusions that ultimately could become very dangerous. For these reasons, it is important to rely on expert knowledge when applying these methods. However, not every user can count on this specific expertise; non-AI-expert users could also benefit from applying these powerful algorithms to their domain problems, but they need basic guidelines to obtain the most out of AI models. The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features. The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering. As a result, 9 papers that tackle AI algorithm recommendation through tangible and traceable rules and heuristics were collected. The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms. [ABSTRACT FROM AUTHOR]

Smart communication has significantly advanced with the integration of the Internet of Things (IoT). Many devices and online services are utilized in the network system to cope with data gathering and forwarding. Recently, many traffic-aware solutions have explored autonomous systems to attain the intelligent routing and flowing of internet traffic with the support of artificial intelligence. However, the inefficient usage of nodes' batteries and long-range communication degrades the connectivity time for the deployed sensors with the end devices. Moreover, trustworthy route identification is another significant research challenge for formulating a smart system. Therefore, this paper presents a smart Random walk Distributed Secured Edge algorithm (RDSE), using a multi-regression model for IoT networks, which aims to enhance the stability of the chosen IoT network with the support of an optimal system. In addition, by using secured computing, the proposed architecture increases the trustworthiness of smart devices with the least node complexity. The proposed algorithm differs from other works in terms of the following factors. Firstly, it uses the random walk to form the initial routes with certain probabilities, and later, by exploring a multi-variant function, it attains long-lasting communication with a high degree of network stability. This helps to improve the optimization criteria for the nodes' communication, and efficiently utilizes energy with the combination of mobile edges. Secondly, the trusted factors successfully identify the normal nodes even when the system is compromised. Therefore, the proposed algorithm reduces data risks and offers a more reliable and private system. In addition, the simulations-based testing reveals the significant performance of the proposed algorithm in comparison to the existing work. [ABSTRACT FROM AUTHOR]

Federated learning (FL) is a scheme in which several consumers work collectively to unravel machine learning (ML) problems, with a dominant collector synchronizing the procedure. This decision correspondingly enables the training data to be distributed, guaranteeing that the individual device's data are secluded. The paper systematically reviewed the available literature using the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guiding principle. The study presents a systematic review of appliable ML approaches for FL, reviews the categorization of FL, discusses the FL application areas, presents the relationship between FL and Blockchain Technology (BT), and discusses some existing literature that has used FL and ML approaches. The study also examined applicable machine learning models for federated learning. The inclusion measures were (i) published between 2017 and 2021, (ii) written in English, (iii) published in a peer-reviewed scientific journal, and (iv) Preprint published papers. Unpublished studies, thesis and dissertation studies, (ii) conference papers, (iii) not in English, and (iv) did not use artificial intelligence models and blockchain technology were all removed from the review. In total, 84 eligible papers were finally examined in this study. Finally, in recent years, the amount of research on ML using FL has increased. Accuracy equivalent to standard feature-based techniques has been attained, and ensembles of many algorithms may yield even better results. We discovered that the best results were obtained from the hybrid design of an ML ensemble employing expert features. However, some additional difficulties and issues need to be overcome, such as efficiency, complexity, and smaller datasets. In addition, novel FL applications should be investigated from the standpoint of the datasets and methodologies. [ABSTRACT FROM AUTHOR]

On the network service platform for vocational education, there are currently over 10,000 online courses. Learners face a challenge in selecting interesting courses from the vast resources available. Learners' urgent need for personalized learning is becoming more apparent as educational informatization progresses. Personalized recommendation (PR) technology can aid personalized learning and increase learners' learning efficiency significantly. This paper constructs a smart classroom model based on AI (artificial intelligence) by studying the connotation and characteristics of smart classroom in light of the current research status and trend of smart classroom at home and abroad. The merits of the recommendation system are determined by the recommendation algorithm used by PR system. This paper primarily focuses on developing a personalized learning platform based on the CF (collaborative filtering) algorithm, as well as conducting system requirements analysis, database design, functional module design, implementation, and testing on this foundation. Experiments are carried out to see if the optimized PR algorithm in the network learning platform is effective. [ABSTRACT FROM AUTHOR]

Many complex physical systems exhibit a rich variety of discrete behavioural modes. Often, the system complexity limits the applicability of standard modelling tools. Hence, understanding the underlying physics of different behaviours and distinguishing between them is challenging. Although traditional machine learning techniques could predict and classify behaviour well, typically they do not provide any meaningful insight into the underlying physics of the system. In this paper we present a novel method for extracting physically meaningful clusters of discrete behaviour from limited experimental observations. This method obtains a set of physically plausible functions that both facilitate behavioural clustering and aid in system understanding. We demonstrate the approach on the V-shaped falling paper system, a new falling paper type system that exhibits four distinct behavioural modes depending on a few morphological parameters. Using just 49 experimental observations, the method discovered a set of candidate functions that distinguish behaviours with an error of 2.04%, while also aiding insight into the physical phenomena driving each behaviour. [ABSTRACT FROM AUTHOR]

Artificial intelligence is an enabling technology for autonomous surface vehicles, with methods such as evolutionary algorithms, artificial potential fields, fast marching methods, and many others becoming increasingly popular for solving problems such as path planning and collision avoidance. However, there currently is no unified way to evaluate the performance of different algorithms, for example with regard to safety or risk. This paper is a step in that direction and offers a comparative study of current state-of-the art path planning and collision avoidance algorithms for autonomous surface vehicles. Across 45 selected papers, we compare important performance properties of the proposed algorithms related to the vessel and the environment it is operating in. We also analyse how safety is incorporated, and what components constitute the objective function in these algorithms. Finally, we focus on comparing advantages and limitations of the 45 analysed papers. A key finding is the need for a unified platform for evaluating and comparing the performance of algorithms under a large set of possible real-world scenarios. [ABSTRACT FROM AUTHOR]

The article focuses on the importance of clinical informatics in neonatal care, highlighting its potential to provide critical resources for clinicians. Topics include the specialized data needed for neonatal care, the challenges in transitioning from paper to electronic health records, and the impact of informatics on real-time patient management and research.

This paper proposes an artificial intelligence-based robust information hiding algorithm to address the issue of confidential information being susceptible to noise attacks during transmission. The algorithm we designed aims to mitigate the impact of various noise attacks on the integrity of secret information during transmission. The method we propose involves encoding secret images into stylized encrypted images and applies adversarial transfer to both the style and content features of the original and embedded data. This process effectively enhances the concealment and imperceptibility of confidential information, thereby improving the security of such information during transmission and reducing security risks. Furthermore, we have designed a specialized attack layer to simulate real-world attacks and common noise scenarios encountered in practical environments. Through adversarial training, the algorithm is strengthened to enhance its resilience against attacks and overall robustness, ensuring better protection against potential threats. Experimental results demonstrate that our proposed algorithm successfully enhances the concealment and unknowability of secret information while maintaining embedding capacity. Additionally, it ensures the quality and fidelity of the stego image. The method we propose not only improves the security and robustness of information hiding technology but also holds practical application value in protecting sensitive data and ensuring the invisibility of confidential information. [ABSTRACT FROM AUTHOR]

Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) algorithms are playing a crucial role in transforming healthcare by enhancing the quality, accessibility, and efficiency of medical care, research, and operations. These algorithms enable healthcare providers to offer more accurate diagnoses, predict outcomes, and customize treatments to individual patient needs. AI also improves operational efficiency by automating routine tasks and optimizing resource management. However, there are challenges to adopting AI in healthcare, such as data privacy concerns and potential biases in algorithms. Collaboration among stakeholders is necessary to ensure ethical use of AI and its positive impact on the field. AI also has applications in medical research, preventive medicine, and public health. It is important to recognize that AI should augment, not replace, the expertise and compassionate care provided by healthcare professionals. The ethical implications and societal impact of AI in healthcare must be carefully considered, guided by fairness, transparency, and accountability principles. Several research papers in this special issue explore the application of AI algorithms in various aspects of healthcare, such as gait analysis for Parkinson's disease diagnosis, human activity recognition, heart disease prediction, compliance assessment with clinical protocols, epidemic management, neurological complications identification, fall prevention, leukemia diagnosis, and genetic clinical pathways. These studies demonstrate the potential of AI in improving medical diagnostics, patient monitoring, and personalized care. [Extracted from the article]

To optimize anthropogenic material streams, the production process, as well as the quality of the products, must be known. With knowledge of these requirements, it is possible to use extra applied algorithms—in this case evolutionary algorithms as part of artificial intelligence—for the optimization of these secondary material streams. The benefit of this application is the fast and precise calculation of the local and global optima of the optimizing problem. This calculation method uses the benefits of the biological reproduction by applications of mutation, selection, and recombination to find one of the best results in a huge amount of possible and potential results. For the use of secondary materials in the paper production it could be proven that in spite of high quotes of secondary materials in different paper classes, there are some paper classes in which the amount of secondary material could be raised without losing any quality. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The paper constructs a massive data mining model of distributed spatiotemporal databases for the Internet of Things. Then a homologous data fusion method based on information entropy is proposed. The storage space required by the tree structure is reduced by constructing the data schema tree of the merged data set. Secondly, the optimal dynamic support degree is obtained by using a neural network and genetic algorithm. Frequent items in the Internet of Things data are mined to achieve the normalization of the clustered feature data based on the threshold value. Experiments show that the F-measure of the data mining algorithm improves the efficiency by 15.64% and 18.25% compared with the kinds of other literatures respectively. RI increased by 21.17% and 26.07%, respectively. [ABSTRACT FROM AUTHOR]

Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper's eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community's early reaction to a paper. [ABSTRACT FROM AUTHOR]

There are great interests of designing research metrics and indices to measure the research impacts in research institutes. Unfortunately, most of those indices ignore critical design issues, e.g. the disparity between domains, the impact of journals or conferences in which papers are published, normalising the range of the index values to certain intervals, and the scalability of using the index to rank different research entities. In this paper, a new normalised fuzzy index, (NFindex), is proposed as a fuzzy-based research impact metric. The proposed index is a scalable index whose values are normalised to the percentage levels. NFindex achieves both inter-discipline normalisation and intra-discipline consistency. The capability of NFindex to achieve the inter-discipline normalisation enables fair comparison between different research domains regardless their nature in terms of influence and contribution to other research areas, e.g. natural science. Therefore, NFindex gives a universal normalised single-number metric that can be used by research institutes to solve the problem of inter-discipline scholar ranking. Moreover, it can help universal ranking of universities and research institutes according to their research capabilities and impacts. The obtained results, on diverse research areas, prove the potential of NFindex in terms of both intra-discipline consistency and inter-discipline normalisation. [ABSTRACT FROM AUTHOR]

This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks mainly use the integration stage to integrate money into the financial system. Fraudsters prefer to launder funds in the early hours, morning followed by the business day's afternoon time intervals. Additionally, the Naïve Bayes and Random Forest classifiers were identified as the two best-performing models to predict bank money laundering transactions. [ABSTRACT FROM AUTHOR]

The development of technologies such as big data and blockchain has brought convenience to life, but at the same time, privacy and security issues are becoming more and more prominent. The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users' privacy by anonymizing big data. However, the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability. In addition, ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced. Based on this, we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data, while guaranteeing improved data usability. Specifically, we construct a new information loss function based on the information quantity theory. Considering that different quasi-identification attributes have different impacts on sensitive attributes, we set weights for each quasi-identification attribute when designing the information loss function. In addition, to reduce information loss, we improve K-anonymity in two ways. First, we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms, i.e., greedy algorithm and 2-means clustering algorithm. In addition, we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass. Meanwhile, we design the K-anonymity algorithm of this scheme based on the constructed information loss function, the improved 2-means clustering algorithm, and the greedy algorithm, which reduces the information loss. Finally, we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) and Machine Learning (ML) have rapidly gained prominence as transformative technologies with immense potential to revolutionize various industries and domains. This research paper presents a comprehensive review of AI and ML, encompassing their fundamental concepts, techniques, and applications. Additionally, it explores recent advancements in the field and offers valuable insights into the future prospects of AI and ML. The paper discusses the historical evolution of AI, the different approaches to AI development, and the components that constitute AI systems. Furthermore, it delves into the core concepts and algorithms of ML, including supervised, unsupervised, and reinforcement learning, as well as the advent of deep learning and neural networks. The applications of AI and ML across diverse domains such as natural language processing, computer vision, healthcare, and finance are also discussed. Recent advancements, such as transfer learning, generative adversarial networks, explainable AI, and federated learning, are highlighted, along with the challenges and limitations faced by these technologies, such as ethical concerns, data quality issues, and interpretability challenges. The paper concludes by presenting future perspectives, including the integration of AI with other technologies, advancements in human-computer interaction, and the impact of quantum computing on ML. This research emphasizes the importance of ongoing research and development in AI and ML and the need to address ethical, security, and interpretability considerations for responsible and beneficial implementation in society. [ABSTRACT FROM AUTHOR]

Deep neural network models have become powerful tools of machine learning and artificial intelligence. They can approximate functions and dynamics by learning from examples. This paper reviews the state-of-art of deep learning-based object detection frameworks that are used for fruit detection in general and for olive fruit in particular. A dataset of olive fruit on the tree is built to train and evaluate deep models. The ultimate goal of this work is the capability of on-edge real-time olive fruit detection on the tree from digital videos. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed You Only Look Once version five (YOLOv5). This paper builds a dataset of 1.2 K source images of olive fruit on the tree and evaluates the latest object detection algorithms focusing on variants of YOLOv5 and YOLOR. The results of the YOLOv5 models show that the YOLOv5 new network models are able to extract rich olive features from images and detect the olive fruit with a high precision of higher than 0.75 mAP_0.5. YOLOv5s performs better for real-time olive fruit detection on the tree over other YOLOv5 variants and YOLOR. [ABSTRACT FROM AUTHOR]

In the field of computer vision, it is a very challenging task to use artificial intelligence deep learning method to realize the programming and creation of NFT artwork. With the continuous development and improvement of deep learning technology, this task has become a reality. The generative adversarial network model used in deep learning can generate new images based on the extraction and analysis of image data features and has become an important tool for NFT artwork image generation. In order to better realize the NFT artwork programming, this paper analyzes the working principle of the traditional adversarial generation method and then uses the StyleGAN model to edit the higher-level attributes of the image, which can effectively control the generated style and style of the NFT artwork image. Finally, in order to improve the quality of the generated images, this paper introduces a channel attention mechanism and a spatial attention mechanism to ensure that the generated images are more reasonable and realistic. Finally, through a large number of experiments, it is proved that the NFT artwork transmission programming algorithm based on artificial intelligence deep learning proposed in this paper can control the overall style of image generation according to the needs of the transmission, and the generated image features have good details and high visual quality. [ABSTRACT FROM AUTHOR]

Understanding of optimization algorithm's behavior is a vital part that is needed for quality progress in the field of stochastic optimization algorithms. To be able to overcome this deficiency, we need to establish new standards for understanding optimization algorithm behavior, which will provide understanding of the working principles behind the stochastic optimization algorithms. In their paper I Evolutionary algorithms and submodular functions: benefits of heavy-tailed mutations i , Quinzan et al. develop suitable Evolutionary Algorithms (EAs) to tackle submodular optimization problems. The paper I Improving convergence in swarm algorithms by controlling range of random movement i by Chaudhary and Banati studies the applicability of the IS technique over different swarm algorithms employing different random distributions. [Extracted from the article]

Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed. [ABSTRACT FROM AUTHOR]

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field. [ABSTRACT FROM AUTHOR]

This review paper discusses the development trends of agricultural autonomous all-terrain vehicles (AATVs) from four cornerstones, such as (1) control strategy and algorithms, (2) sensors, (3) data communication tools and systems, and (4) controllers and actuators, based on 221 papers published in peer-reviewed journals for 1960–2023. The paper highlights a comparative analysis of commonly employed control methods and algorithms by highlighting their advantages and disadvantages. It gives comparative analyses of sensors, data communication tools, actuators, and hardware-embedded controllers. In recent years, many novel developments in AATVs have been made due to advancements in wireless and remote communication, high-speed data processors, sensors, computer vision, and broader applications of AI tools. Technical advancements in fully autonomous control of AATVs remain limited, requiring research into accurate estimation of terrain mechanics, identifying uncertainties, and making fast and accurate decisions, as well as utilizing wireless communication and edge cloud computing. Furthermore, most of the developments are at the research level and have many practical limitations due to terrain and weather conditions. [ABSTRACT FROM AUTHOR]

With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average. [ABSTRACT FROM AUTHOR]

Simple Summary: This paper delves into the persistent issue of unequal access to medical imaging, with a particular focus on breast cancer screening and its impact on marginalized communities and racial/ethnic minorities. Central to our discussion is the role of scientific mobility among radiologists in fostering healthcare policy changes that promote diversity and cultural competence. We propose various strategies to bridge this gap, including cultural education, sensitivity training, and diversifying the radiology workforce. These measures aim to improve communication with diverse patient groups and reduce healthcare disparities. Additionally, we explore the challenges and advantages of teleradiology as a means to extend medical imaging services to underserved areas. In the context of artificial intelligence, we emphasize the critical need to validate algorithms across diverse populations to ensure unbiased and equitable healthcare outcomes. Overall, this paper underscores the importance of international collaboration in addressing global access barriers, presenting it as a key to mitigating disparities in medical imaging access and contributing to the pursuit of equitable healthcare. Access to medical imaging is pivotal in healthcare, playing a crucial role in the prevention, diagnosis, and management of diseases. However, disparities persist in this scenario, disproportionately affecting marginalized communities, racial and ethnic minorities, and individuals facing linguistic or cultural barriers. This paper critically assesses methods to mitigate these disparities, with a focus on breast cancer screening. We underscore scientific mobility as a vital tool for radiologists to advocate for healthcare policy changes: it not only enhances diversity and cultural competence within the radiology community but also fosters international cooperation and knowledge exchange among healthcare institutions. Efforts to ensure cultural competency among radiologists are discussed, including ongoing cultural education, sensitivity training, and workforce diversification. These initiatives are key to improving patient communication and reducing healthcare disparities. This paper also highlights the crucial role of policy changes and legislation in promoting equal access to essential screening services like mammography. We explore the challenges and potential of teleradiology in improving access to medical imaging in remote and underserved areas. In the era of artificial intelligence, this paper emphasizes the necessity of validating its models across a spectrum of populations to prevent bias and achieve equitable healthcare outcomes. Finally, the importance of international collaboration is illustrated, showcasing its role in sharing insights and strategies to overcome global access barriers in medical imaging. Overall, this paper offers a comprehensive overview of the challenges related to disparities in medical imaging access and proposes actionable strategies to address these challenges, aiming for equitable healthcare delivery. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning is an artificial intelligence technology that combines deep learning and reinforcement learning and has been widely applied in multiple fields. As a type of deep reinforcement learning algorithm, the A3C (Asynchronous Advantage Actor-Critic) algorithm can effectively utilize computer resources and improve training efficiency by synchronously training Actor-Critic in multiple threads. Inspired by the excellent performance of the A3C algorithm, this paper uses the A3C algorithm to solve the UUV (Unmanned Underwater Vehicle) collision avoidance planning problem in unknown environments. This collision avoidance planning algorithm can have the ability to plan in real-time while ensuring a shorter path length, and the output action space can meet the kinematic constraints of UUVs. In response to the problem of UUV collision avoidance planning, this paper designs the state space, action space, and reward function. The simulation results show that the A3C collision avoidance planning algorithm can guide a UUV to avoid obstacles and reach the preset target point. The path planned by this algorithm meets the heading constraints of the UUV, and the planning time is short, which can meet the requirements of real-time planning. [ABSTRACT FROM AUTHOR]

The papers are grouped into three categories: AI methods for manufacturing systems, AI developments specifically in semiconductor manufacturing, and AI in additive manufacturing and maintenance. They combine a deep neural network model and Markov decision processes (MDP) to rapidly generate near optimal dynamic control policies for problems that are too large to be only solved by MDP, thus showing the potential of machine learning in controlling unreliable manufacturing systems. [Extracted from the article]

Given the increasingly important role that the use of artificial intelligence algorithms is taking on in the medical field today (especially in oncology), the purpose of this systematic review is to analyze the main reports on such algorithms applied for the prognostic evaluation of patients with head and neck malignancies. The objective of this paper is to examine the currently available literature in the field of artificial intelligence applied to head and neck oncology, particularly in the prognostic evaluation of the patient with this kind of tumor, by means of a systematic review. The paper exposes an overview of the applications of artificial intelligence in deriving prognostic information related to the prediction of survival and recurrence and how these data may have a potential impact on the choice of therapeutic strategy, making it increasingly personalized. This systematic review was written following the PRISMA 2020 guidelines. [ABSTRACT FROM AUTHOR]