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Recently, computer vision and artificial intelligence are being used as enabling technologies for plant phenotyping studies, since they allow the analysis of large amounts of data gathered by the sensors. Plant phenotyping studies can be devoted to the evaluation of complex plant traits either on the aerial part of the plant as well as on the underground part, to extract meaningful information about the growth, development, tolerance, or resistance of the plant itself. All plant traits should be evaluated automatically and quantitatively measured in a non-destructive way. This paper describes a novel approach for identifying plant roots from images of the root system architecture using a convolutional neural network (CNN) that operates on small image patches calculating the probability that the center point of the patch is a root pixel. The underlying idea is that the CNN model should embed as much information as possible about the variability of the patches that can show chaotic and heterogeneous backgrounds. Results on a real dataset demonstrate the feasibility of the proposed approach, as it overcomes the current state of the art. • Root systems must be monitored to assess the growth and well-being of a plant. • State-of-the-art approaches mainly use classic ML or U-networks for segmentation. • CNNs can be used for monitoring considering patch-based information. • These models are simpler and faster, and provide better segmentation performance. [ABSTRACT FROM AUTHOR]

The absence of population-stratified cardiovascular magnetic resonance (CMR) reference ranges from large cohorts is a major shortcoming for clinical care. This paper provides age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics from the Healthy Hearts Consortium, an international collaborative comprising 9,088 CMR studies from verified healthy individuals, covering the complete adult age spectrum across both sexes, and with the highest ethnic diversity reported to date. CMR studies were analyzed using certified software with batch processing capability (cvi42, version 5.14 prototype, Circle Cardiovascular Imaging) by 2 expert readers. Three segmentation methods (smooth, papillary, anatomic) were used to contour the endocardial and epicardial borders of the ventricles and atria from long- and short-axis cine series. Clinically established ventricular and atrial metrics were extracted and stratified by age, sex, and ethnicity. Variations by segmentation method, scanner vendor, and magnet strength were examined. Reference ranges are reported as 95% prediction intervals. The sample included 4,452 (49.0%) men and 4,636 (51.0%) women with average age of 61.1 ± 12.9 years (range: 18-83 years). Among these, 7,424 (81.7%) were from White, 510 (5.6%) South Asian, 478 (5.3%) mixed/other, 341 (3.7%) Black, and 335 (3.7%) Chinese ethnicities. Images were acquired using 1.5-T (n = 8,779; 96.6%) and 3.0-T (n = 309; 3.4%) scanners from Siemens (n = 8,299; 91.3%), Philips (n = 498; 5.5%), and GE (n = 291, 3.2%). This work represents a resource with healthy CMR-derived volumetric reference ranges ready for clinical implementation. [Display omitted] [ABSTRACT FROM AUTHOR]

X-ray computed tomography (XCT) has been shown to be a reliable tool for quality inspection, material evaluation, and dimensional measurement tasks across diverse academic and industrial applications. In recent years, the integration of machine learning and deep learning techniques have ushered new advances in the industrial computed tomography domain spanning multiple facets, including image reconstruction, segmentation, and feature characterization. This review paper comprehensively surveys the current state-of-the-art machine learning and deep learning applications throughout the entire XCT workflow. Additionally, we explore relevant developments in the medical imaging domain, evaluating their implications for industrial computed tomography. In conclusion, we identify potential future research, drawing insights from existing research gaps in the domain and recent advancements in artificial intelligence. Notably, we underscore the importance of uncertainty quantification and model explainability for further acceptance of artificial intelligence techniques in the domain. [ABSTRACT FROM AUTHOR]

We trust it is clear that JBI addresses a broader range of methods than AI, whereas the other two journals are singularly focused on AI and data science. With the growth in Artificial Intelligence in Medicine (AIM) research and the plethora of informatics journals, there is some confusion where to direct an AIM-related manuscript for peer review and possible publication. [Extracted from the article]

Artificial Intelligence and Machine learning have been widely used in various fields of mathematical computing, physical modeling, computational science, communication science, and stochastic analysis. Approaches based on Deep Artificial Neural Networks (DANN) are very popular in our days. Depending on the learning task, the exact form of DANNs is determined via their multi-layer architecture, activation functions and the so-called loss function. However, for a majority of deep learning approaches based on DANNs, the kernel structure of neural signal processing remains the same, where the node response is encoded as a linear superposition of neural activity, while the non-linearity is triggered by the activation functions. In the current paper, we suggest to analyze the neural signal processing in DANNs from the point of view of homogeneous chaos theory as known from polynomial chaos expansion (PCE). From the PCE perspective, the (linear) response on each node of a DANN could be seen as a 1st degree multi-variate polynomial of single neurons from the previous layer, i.e. linear weighted sum of monomials. From this point of view, the conventional DANN structure relies implicitly (but erroneously) on a Gaussian distribution of neural signals. Additionally, this view revels that by design DANNs do not necessarily fulfill any orthogonality or orthonormality condition for a majority of data-driven applications. Therefore, the prevailing handling of neural signals in DANNs could lead to redundant representation as any neural signal could contain some partial information from other neural signals. To tackle that challenge, we suggest to employ the data-driven generalization of PCE theory known as arbitrary polynomial chaos (aPC) to construct a corresponding multi-variate orthonormal representations on each node of a DANN. Doing so, we generalize the conventional structure of DANNs to Deep arbitrary polynomial chaos neural networks (DaPC NN). They decompose the neural signals that travel through the multi-layer structure by an adaptive construction of data-driven multi-variate orthonormal bases for each layer. Moreover, the introduced DaPC NN provides an opportunity to go beyond the linear weighted superposition of single neurons on each node. Inheriting fundamentals of PCE theory, the DaPC NN offers an additional possibility to account for high-order neural effects reflecting simultaneous interaction in multi-layer networks. Introducing the high-order weighted superposition on each node of the network mitigates the necessity to introduce non-linearity via activation functions and, hence, reduces the room for potential subjectivity in the modeling procedure. Although the current DaPC NN framework has no theoretical restrictions on the use of activation functions. The current paper also summarizes relevant properties of DaPC NNs inherited from aPC as analytical expressions for statistical quantities and sensitivity indexes on each node. We also offer an analytical form of partial derivatives that could be used in various training algorithms. Technically, DaPC NNs require similar training procedures as conventional DANNs, and all trained weights determine automatically the corresponding multi-variate data-driven orthonormal bases for all layers of DaPC NN. The paper makes use of three test cases to illustrate the performance of DaPC NN, comparing it with the performance of the conventional DANN and also with plain aPC expansion. Evidence of convergence over the training data size against validation data sets demonstrates that the DaPC NN outperforms the conventional DANN systematically. Overall, the suggested re-formulation of the kernel network structure in terms of homogeneous chaos theory is not limited to any particular architecture or any particular definition of the loss function. The DaPC NN Matlab Toolbox is available online and users are invited to adopt it for own needs. • Kernel structure reformulation of deep artificial neural networks in terms of homogeneous chaos theory. • Response on each node of the deep network represented through polynomial chaos expansion. • Orthonormal decomposition mitigates the non-optimal representation while processing the neural signal. • Accounting for high-order effects reflecting simultaneous neural impacts. • Analytical estimation of mean and variance of neural signal on each node is provided. [ABSTRACT FROM AUTHOR]

Despite lacking a generally accepted definition, Artificial General Intelligence (AGI) is commonly understood to refer to artificial agents possessing the capacity to build up a context-independent understanding of itself and the world and to generalize this knowledge across a multitude of contexts. In human agents, this capacity is, to a large degree, facilitated by processes of self-directed learning , during which agents voluntarily control the conditions under which episodes of learning and problem solving occur. Since self-directed learning depends on the degree of knowledge the agent has about various aspects of themselves (their bodily skills, their learning goal, etc.), an AGI implementation of this type of learning must build on a theory of how this self-knowledge is actualized and modified during the learning process. In this paper, we employ the pattern theory of self in order to characterize different aspects of an agent's self that are relevant for self-directed learning. Such aspects include agent-internal cognitive states such as thoughts, emotions, and intentions, but also relational states such as action possibilities in the environment. Combinations of these aspects form a characteristic pattern, which is unique to each individual agent, with no one aspect being necessary or sufficient for the individuation of that agent's self. Here, we focus on the interdependence of narrative and embodied aspects of the self-pattern, since they involve particularly salient challenges consisting in conceptualizing the interaction between propositional and motor representations. In our paper, we model the reciprocal interaction of these aspects of the self-pattern within an individual cognitive agent. We do so by extending an approach by Ryan, Agrawal, & Franklin (2020), who laid the groundwork for the implementation of the pattern theory of self in the LIDA (Learning Intelligent Decision Agent) model. We describe how embodied and narrative aspects of an agent's self-pattern are realized by patterns of interaction between different LIDA modules over time, and how interactions at multiple temporal scales allow the agent's self-pattern to be both dynamically variable and relatively stable. Finally, we investigate the implications this view has for the creation of artificial agents that can benefit from self-directed learning, both in the context of deliberate planning and adaptive motor execution. [ABSTRACT FROM AUTHOR]

Significant advances have been made in artificial intelligence technology in recent years. Many health care applications have been investigated to assist clinicians and the technology is close to being integrated into routine clinical practice. The high prevalence of cardiac disease in Australia places overwhelming demands on the existing health care system, challenging its capacity to provide quality patient care. Artificial intelligence has emerged as a promising solution. This discussion paper provides an Australian perspective on the current state of artificial intelligence in cardiology, including the benefits and challenges of implementation. This paper highlights some current artificial intelligence applications in cardiology, while also detailing challenges such as data privacy, ethical considerations, and integration within existing health infrastructures. Overall, this paper aims to provide insights into the potential benefits of artificial intelligence in cardiology, while also acknowledging the barriers that need to be addressed to ensure safe and effective implementation into an Australian health system. [ABSTRACT FROM AUTHOR]

Motion perception is an essential ability for animals and artificially intelligent systems interacting effectively, safely with surrounding objects and environments. Biological visual systems, that have naturally evolved over hundreds-million years, are quite efficient and robust for motion perception, whereas artificial vision systems are far from such capability. This paper argues that the gap can be significantly reduced by formulation of ON/OFF channels in motion perception models encoding luminance increment (ON) and decrement (OFF) responses within receptive field, separately. Such signal-bifurcating structure has been found in neural systems of many animal species articulating early motion is split and processed in segregated pathways. However, the corresponding biological substrates, and the necessity for artificial vision systems have never been elucidated together, leaving concerns on uniqueness and advantages of ON/OFF channels upon building dynamic vision systems to address real world challenges. This paper highlights the importance of ON/OFF channels in motion perception through surveying current progress covering both neuroscience and computationally modelling works with applications. Compared to related literature, this paper for the first time provides insights into implementation of different selectivity to directional motion of looming, translating, and small-sized target movement based on ON/OFF channels in keeping with soundness and robustness of biological principles. Existing challenges and future trends of such bio-plausible computational structure for visual perception in connection with hotspots of machine learning, advanced vision sensors like event-driven camera finally are discussed. [ABSTRACT FROM AUTHOR]

• Identify gaps in state-of-the-art research to support healthcare AI trustworthiness. • Explore the explainable AI and fusion in healthcare. • Explore legitimacy, morality, and robustness standards for AI policymakers. • Assessment quality, bias risk, and data fusion for medical AI trustworthiness. • Examine eight research gaps to guide future study and assist researchers. In the last few years, the trend in health care of embracing artificial intelligence (AI) has dramatically changed the medical landscape. Medical centres have adopted AI applications to increase the accuracy of disease diagnosis and mitigate health risks. AI applications have changed rules and policies related to healthcare practice and work ethics. However, building trustworthy and explainable AI (XAI) in healthcare systems is still in its early stages. Specifically, the European Union has stated that AI must be human-centred and trustworthy, whereas in the healthcare sector, low methodological quality and high bias risk have become major concerns. This study endeavours to offer a systematic review of the trustworthiness and explainability of AI applications in healthcare, incorporating the assessment of quality, bias risk, and data fusion to supplement previous studies and provide more accurate and definitive findings. Likewise, 64 recent contributions on the trustworthiness of AI in healthcare from multiple databases (i.e., ScienceDirect, Scopus, Web of Science, and IEEE Xplore) were identified using a rigorous literature search method and selection criteria. The considered papers were categorised into a coherent and systematic classification including seven categories: explainable robotics, prediction, decision support, blockchain, transparency, digital health, and review. In this paper, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth the challenges, motivations, and recommendations. In this study a systematic science mapping analysis in order to reorganise and summarise the results of earlier studies to address the issues of trustworthiness and objectivity was also performed. Moreover, this work has provided decisive evidence for the trustworthiness of AI in health care by presenting eight current state-of-the-art critical analyses regarding those more relevant research gaps. In addition, to the best of our knowledge, this study is the first to investigate the feasibility of utilising trustworthy and XAI applications in healthcare, by incorporating data fusion techniques and connecting various important pieces of information from available healthcare datasets and AI algorithms. The analysis of the revised contributions revealed crucial implications for academics and practitioners, and then potential methodological aspects to enhance the trustworthiness of AI applications in the medical sector were reviewed. Successively, the theoretical concept and current use of 17 XAI methods in health care were addressed. Finally, several objectives and guidelines were provided to policymakers to establish electronic health-care systems focused on achieving relevant features such as legitimacy, morality, and robustness. Several types of information fusion in healthcare were focused on in this study, including data, feature, image, decision, multimodal, hybrid, and temporal. [ABSTRACT FROM AUTHOR]

[Display omitted] In order to assess the research status, hot issues of artificial intelligence in nursing to provide reference for scholars. This work conduct a quantitative analysis on related literature in WOS from 2011 to 2022 by mathematical and statistical methods, including publication trend, journals, author, institution, national and regional, keyword, literature co-citation. The hotspots and trends were revealed. The results showed that: (1) scholars' attention to it showed a steady increasing. (2) There were 20 journals published more than 6 papers, published 205 papers, that's 30.83 % of the total, Journal of Healthcare Engineering published the most papers, that's 8.78 %. (2) Kendrick Cato, Lisiane Pruinelli published the most papers, that's 2.95 %, which have strong cooperation. (3) Harvard Medical School, Columbia University and University of Minnesota published 114 papers, that's 8.21 % of the total, were the core unit, Harvard Med Sch, Columbia Univ and Univ Penn had strong cooperation. (4) The United States, China, Japan, Australia and United Kingdom are the top five countries for publishing papers, that's 80.3 % of the total. The cooperation degree of the each are 0.51, 0.36, 0.07, 0.04, and 0.06. (6) "electronic health record", "risk prediction" and "supervised machine learning" are current research hotspots. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has revolutionized various domains, including education and research. Natural language processing (NLP) techniques and large language models (LLMs) such as GPT-4 and BARD have significantly advanced our comprehension and application of AI in these fields. This paper provides an in-depth introduction to AI, NLP, and LLMs, discussing their potential impact on education and research. By exploring the advantages, challenges, and innovative applications of these technologies, this review gives educators, researchers, students, and readers a comprehensive view of how AI could shape educational and research practices in the future, ultimately leading to improved outcomes. Key applications discussed in the field of research include text generation, data analysis and interpretation, literature review, formatting and editing, and peer review. AI applications in academics and education include educational support and constructive feedback, assessment, grading, tailored curricula, personalized career guidance, and mental health support. Addressing the challenges associated with these technologies, such as ethical concerns and algorithmic biases, is essential for maximizing their potential to improve education and research outcomes. Ultimately, the paper aims to contribute to the ongoing discussion about the role of AI in education and research and highlight its potential to lead to better outcomes for students, educators, and researchers. [ABSTRACT FROM AUTHOR]

Stress, a pervasive issue in today's world, significantly impacts mental and physical health. With approximately 20% of adults experiencing mental health disorders each year, and depression affecting over 260 million people globally, effective stress manage- ment is crucial. Chronic stress is also associated with physical ailments, including cardiovascular diseases, the leading global cause of death. In parallel, the wearable device market has witnessed substantial growth, with over 300 million units sold worldwide in 2021. These devices offer a unique opportunity to collect physiological and behavioral data, making them invaluable for stress detection and prediction. Combining artificial intelligence (AI) and wearables, this paper explores their accuracy in capturing vital data and the potential to revolutionize stress management, ultimately improving mental and physical well-being. In this paper, the ensemble-based classifier known as the Histogram-based Gradient Boosting Classifier detected stress from heart rates with an accuracy of 73%. This establishes the independent nature of Heart Rates for stress prediction, emphasizing that there is no need for sophisticated ECG and HRV readings. This opens up the possibility of using sensors as used in wearable devices to effectively detect stress in our day-to-day lives. [ABSTRACT FROM AUTHOR]

In the digital age, organizations confront the challenge of managing diverse documents efficiently while ensuring security, accuracy, and accessibility. Conventional document management approaches often must catch up, leading to inefficiencies and increased costs. This paper introduces the Intelligent Document Management System (IDMS), which employs advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR) to enhance document workflows. This research extends the capabilities of IDMS to encompass the extraction and processing of data from three important document types: medical bills, Aadhar cards, and PAN cards. The research and development efforts done in this paper have concentrated on seamlessly integrating of these models into the IDMS framework, offering a comprehensive solution for extracting and processing data from various document types. In this paper, two approaches, namely Easy OCR and a hybrid approach of combining NLP (Regular Expression) and CV (OCR) have been applied and compared. The results revealed that the proposed hybrid approach (NLPCV) is better, with higher accuracy of 97%,71 %, and 78% for hospital invoices, Aadhar cards, and PAN cards, respectively. [ABSTRACT FROM AUTHOR]

The process of miner selection in blockchain networks plays a significant role in maintaining the efficiency and security of the net- work. In blockchain, the consensus algorithm is crucial for miner selection. Considering the downsides of the consensus algorithm (PoW, PoS) this paper employs proof of artificial intelligence (PoAI) over proof of stake and proof of work. This paper proposes an approach for miner selection using the Proof of Artificial Intelligence (PoAI) framework, which leverages Deep Neural Networks (DNNs) to predict efficient miners for blockchain networks. This paper describes the design and implementation of our PoAI that uses DNN algorithm on a bitcoin dataset, is a combination of supervised and unsupervised learning techniques to model the be- havior of miners using suitable features such as age, number of blocks mined, transaction volume and number of transactions. The proposed methodology improves the scalability of the consensus algorithm. [ABSTRACT FROM AUTHOR]

This volume presents selected papers of the 12th Young Scientists Conference in Computational Science (YSC'2023). ITMO University annually organises the event with various academic partners to disseminate current trends in Artificial Intelligence and Computational science among young researchers. In this paper, we present our view on major trends and challenges today in front of scientific and industrial society in this promising area. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has the potential to simplify and optimize various steps of the brachytherapy workflow, and this literature review aims to provide an overview of the work done in this field. We conducted a literature search in June 2022 on PubMed, Embase, and Cochrane for papers that proposed AI applications in brachytherapy. A total of 80 papers satisfied inclusion/exclusion criteria. These papers were categorized as follows: segmentation (24), registration and image processing (6), preplanning (13), dose prediction and treatment planning (11), applicator/catheter/needle reconstruction (16), and quality assurance (10). AI techniques ranged from classical models such as support vector machines and decision tree-based learning to newer techniques such as U-Net and deep reinforcement learning, and were applied to facilitate small steps of a process (e.g., optimizing applicator selection) or even automate the entire step of the workflow (e.g., end-to-end preplanning). Many of these algorithms demonstrated human-level performance and offer significant improvements in speed. AI has potential to augment, automate, and/or accelerate many steps of the brachytherapy workflow. We recommend that future studies adhere to standard reporting guidelines. We also stress the importance of using larger sample sizes and reporting results using clinically interpretable measures. [ABSTRACT FROM AUTHOR]

The constrained disorder principle (CDP) defines systems based on their degree of disorder bounded by dynamic boundaries. The principle explains stochasticity in living and non-living systems. Denis Noble described the importance of stochasticity in biology, emphasizing stochastic processes at molecular, cellular, and higher levels in organisms as having a role beyond simple noise. The CDP and Noble's theories (NT) claim that biological systems use stochasticity. This paper presents the CDP and NT, discussing common notions and differences between the two theories. The paper presents the CDP-based concept of taking the disorder beyond its role in nature to correct malfunctions of systems and improve the efficiency of biological systems. The use of CDP-based algorithms embedded in second-generation artificial intelligence platforms is described. In summary, noise is inherent to complex systems and has a functional role. The CDP provides the option of using noise to improve functionality. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet leads to the rapid growth of network information, we call it information explosion. The Internet is full of information, and it is difficult for users to find this information and useful knowledge of the ocean. The Web has become the world's largest information repository, and there is an urgent need for efficient access to the valuable knowledge of vast amounts of web information. The purpose of this paper is to study the process and algorithm analysis of text mining system based on artificial intelligence. This paper presents an algorithm of document feature acquisition based on genetic algorithm. Selecting suitable features is an important task in specific text classification and information retrieval. Finding appropriate feature vectors to represent the text will undoubtedly help with subsequent sorting and grouping. Based on the genetic algorithm of variable length chromosome, this paper improves the crossover, mutation and selection operations, and proposes an algorithm to obtain text feature vectors. This method has a wide range of applications and good results. [ABSTRACT FROM AUTHOR]

Because of the complexity and development of network information data processing, many conventional technical means can not process information data, and the artificial itself is powerless, so it needs to use artificial intelligence technology for processing. In view of this, this paper will introduce the basic concept of artificial intelligence technology and its advantages in information data processing, and then combine the technology to put forward the network information data processing method. Through the research, the method in this paper can give full play to the role of artificial intelligence technology, fast and in-depth processing of network information data, and can give artificial decision support. [ABSTRACT FROM AUTHOR]

With the rapid growth of social economy, the requirements for resource utilization are becoming higher and higher. How to effectively solve the problem of energy shortage has become a hot topic. However, there are still many deficiencies in the current resource scheduling algorithm. For example, for different types of satellites with different needs and requirements, their performance indicators are different, and in the satellite communication system, the interaction between multiple nodes needs to be considered. Therefore, this paper studies the artificial intelligence algorithm, and proposes an improved particle complementary scheduling strategy for a single user in view of the shortcomings of the existing particle swarm optimization (PSO) algorithm. On this basis, this paper applies this method to evaluate the system performance and verify its effectiveness. The verification results show that the utilization efficiency of the satellite communication system resource scheduling algorithm is above 93%. [ABSTRACT FROM AUTHOR]

Since people have limited ability to judge the quality of their own singing and pronunciation, it is difficult to find problems on their own after reaching a certain level, so they need external help. However, external help also comes from human beings in the past and has many limitations. Therefore, in order to provide better help, this paper will carry out research from the perspective of artificial intelligence. In this paper, the basic concepts of artificial intelligence technology are discussed, and then the evaluation ideas of singing pronunciation quality on the technical level are put forward. Finally, the method system is designed. [ABSTRACT FROM AUTHOR]

• AI improves infectious waste management. • Ensemble model exceeds 97.81% accuracy. • Reinforcement learning optimizes classification. • Geometric augmentation boosts model robustness. • Study addresses urgent urban waste challenges. This research paper focuses on effective infectious municipal waste management in urban settings, highlighting a dearth of dedicated research in this domain. Unlike general or specific waste types, infectious waste poses distinct health and environmental risks. Leveraging advanced artificial intelligence techniques, we prioritize infectious waste categorization and optimization, integrating metaheuristics into optimization methods to create a robust dual-ensemble framework. Our model, the "Enhanced Artificial Intelligence for Infectious Municipal Waste Classification System," combines ensemble image segmentation methods and diverse convolutional neural network models. Innovative geometric image augmentation enhances model robustness, diversifies training data, and improves accuracy across waste types. A pivotal aspect is the integration of a reinforcement learning-differential evolution algorithm as a decision fusion strategy, optimizing classification by harmonizing outputs from ensemble methods and convolutional neural network models. Computational results, using a newly collected dataset, demonstrate our model's accuracy exceeding 96.54% while using the existing solid waste dataset we achieve the accuracy of 97.81%, outperforming advanced approaches by margins ranging from 2.02% to 8.80%. This research significantly advances sustainable waste management, showcasing artificial intelligence's transformative potential in addressing intricate waste challenges. It establishes a foundational framework prioritizing efficiency, effectiveness, and sustainability for future waste management solutions. Acknowledging the importance of diverse datasets, customization for urban contexts, and practical integration into existing infrastructures, our work contributes to the broader discourse on the role of artificial intelligence in evolving waste management practices. [ABSTRACT FROM AUTHOR]

With the artificial intelligence technology development boom, large language models are demonstrating their potential in comprehension and creativity. Large language models such as GPT-4 and Gemini have been able to powerfully study for various professional-level exams. However, as a language model itself, its powerful comprehension can only be reflected in text sequences. Currently, although videos can be generated through the connection between 3D point clouds and large language models, there is currently no prompt project that directly interacts with one-dimensional through attribute calculation results. The point cloud data is also rich in information that can support various tasks of urban construction. For scene-level point cloud data, there has been a lot of research done on semantic segmentation, target detection, and other tasks. However, it is usually difficult to provide direct help to scene construction from the perception results. This paper presents a method for applying large language models to urban ecological construction by combining the results of 3D point cloud semantic segmentation. The objective is to integrate the prior knowledge and creative capabilities of Large Language Models (LLMs) within urban development with the outcomes derived from point cloud semantic segmentation results. This integration aims to establish an interactive point cloud intelligent analysis system, tailored for aiding decision-making processes in urban ecological civilization construction, thus presenting innovative perspectives for the advancement of smart city development. • Chat3D, a large model for interactive large 3D scene understanding to urban ecological construction based on prompt engineering. • Utilizing thought chains, different levels of prompts input improve the completeness and credibility of report generation. • Experiments show that Chat3D accurately predicts urban environmental indices, assesses urban ecological risks, and benefits sustainable urban development. [ABSTRACT FROM AUTHOR]

• Research on possible scenarios of implementing AI in nanosatellite constellations. • Challenges, applications and possible strategies in Low Earth Orbits, Transfer Region and Lunar Orbits is discussed. • Several examples and major necessities have been included. • Figures portray severe issues to applications and strategic ways to adopt the AI in nanosat constraints is shown in detail. • The future prospects and ways to step-wise organize and implement AI is discussed. The small satellites have been changing the paradigm of space technology and implementation techniques ever since the NewSpace era has begun a stiff competition and dynamic atmosphere for innovation and recreation. Nanosatellites (ranging from 1 to 10 Kg) have been of significant importance since they have been launched and experimented in the past few decades. The constellations of these satellites will be a test of technology and management in due course of time. The complexity of operations and skilled functioning of the satellites in LEO and beyond is becoming crucial for the space industry and scientific communities. This paper will demonstrate the challenges, applications, and strategies to implement Artificial Intelligence (AI) optimally to make these constellations efficient and productive under the given scenarios and constraints. A thorough investigation through literature study has been initially done to observe and monitor the recent developments and envision the futuristic implementation of cognitive computing in CisLunar space. Hence, a detailed description of strategizing this artificial intelligence implementation in three classified regions as Low Earth Orbits (LEO), Transfer Region (TR), and Lunar Orbits (LO) to emphasize their crucial role in operating nanosatellites is significantly presented. [ABSTRACT FROM AUTHOR]

The utilization of artificial intelligence (AI) in medical imaging has become a rapidly growing field as a means to address contemporary demands and challenges of healthcare. Among the emerging applications of AI is point-of-care ultrasound (POCUS), in which the combination of these two technologies has garnered recent attention in research and clinical settings. In this Controversies paper, we will discuss the benefits, limitations, and future considerations of AI in POCUS for patients, clinicians, and healthcare systems. [ABSTRACT FROM AUTHOR]

The patient concerns inventory (PCI) allows patients to highlight the issues they would like to discuss at their outpatient consultation. It improves patient-clinician communication and has proven benefits. While the PCI is effective, patient experiences could be improved with better access to it and the ability to more easily and frequently express their concerns. This, of course, is in the context of ever-increasing healthcare challenges and limited resources. Use of conversational artificial intelligence (CAI) represents an opportunity to improve information flow between patients and professionals remote from the consultation. This paper highlights the potential for CAI to provide an 'always-on' platform, using natural language interface technology and based on the PCI, which patients can access via their mobile devices. We also discuss potential pitfalls and concerns, along with outlining a current clinical trial assessing, in the first instance, usability of this technology. [ABSTRACT FROM AUTHOR]

The development of the computer and what is now known as artificial intelligence (AI) has evolved over more than two centuries in a long series of steps. The date of the invention of the first computer is estimated at 1822, when Charles Babbage (1791-1871) developed his first design of a working computer on paper, based mainly on a Jacquard loom. He worked on his project together with Augusta Ada King, Countess Lovelace (née Byron) (Ada Lovelace) (1815-1852), whom he called the "Sorceress of Numbers." This work will present the profile and achievements of Charles Babbage, Augusta Ada King, Countess Lovelace, and Alan Mathison Turing (1912 - 1954), who is considered the father of computer science and artificial intelligence, and then provide an outline of the tumultuous events affecting AI up to the present. [ABSTRACT FROM AUTHOR]

Precision Agriculture (PA) has revolutionized crop management by leveraging information technology, satellite positioning data, and remote sensing. One crucial component in PA applications is the Normalized Difference Vegetation Index (NDVI), which offers valuable insights into crop vigor and health. However, discontinuity of optical satellite acquisitions related to cloud cover and the huge load of the required processing time pose challenges to real-time applications. NDVI prediction emerges as an innovative solution to address these limitations. It allows for proactive decision-making by providing accurate estimates, enabling farmers and land managers to plan essential agronomic activities such as irrigation, fertilization, and pest control, based on anticipated future conditions. This study introduces an Artificial Neural Network (ANN) model incorporating NDVI, Normalized Difference Water Index (NDWI), temperatures, and precipitation as predictive variables. The model employs a novel time series slicing algorithm, Boosting Adaptive Time Series Slicer (BATS), to enhance the input training dataset's variability, presenting the model with a broader range of examples. A 2-Bidirectional Long Short-Term Memory (LSTM) forecasting model was developed to predict future NDVI values over short and medium-term horizons. The study area used to train, test and validate the ANN corresponds to a diverse landscape of cultivated corn fields located in Piemonte (NW-Italy). Results showed that NDVI future estimates were accurate; considering three time horizons for predictions (5, 10, and 15 days) RMSE values resulted to be 0.028, 0.038 and 0.050, respectively. Additionally, ablation tests proved that the most important variable for enhancing the model's accuracy is the NDWI, and the most useful timesteps are the four most recent ones. To preliminary investigate the capability of the ANN to operate over a wider and different area it was applied over the entire Europe, using the LUCAS dataset as reference map to locate corn fields. Results show RMSE of 0.062, 0.083 and 0.105 for the 5, 10 and 15 days forecasting horizons, respectively. The methodology proposed in this paper can be a possible alternative to more ordinary approaches for NDVI forecasting that nowadays appears to be a fundamental step for a proactive precision agriculture where crop management can be significantly improved. Future developments should explore the use of sequence-to-sequence ANNs to predict the development of multiple spectral indices over multiple crop types simultaneously. [ABSTRACT FROM AUTHOR]

In this paper, we employ the language ontologies framework to artificial intelligence (specifically, OpenAI's ChatGPT) to investigate the 'ethnographic encounter' between human and non-human language users. Our focus is on the exchange and interplay between human language users and non-human artificial language generators in the production of written text. We analyse how such programs transform our understanding of what language is or might be; their practices to create language are unfamiliar, and yet they make sense to human interlocutors. Drawing from, and building on, the language ontologies framework, we discuss the practices involved in such encounters and suggest the need for an updated 'toolkit' in our understanding of language to account for transhuman interactions. • Language is many things, depending on how it is practiced and performed. • Ontologies allow us to approach language(s)/languaging as an anthropologist approaches an 'ethnographic encounter'. • The interaction between humans and artificial intelligence (AI) is an ethnographic encounter used to explore transhuman language ontologies. • The ways in which AI generates language is responsive to human linguistic behaviour; however, the way AI creates language is unique. • Language patterns, or wordings , constitute a significant element of the languaging process for ChatGPT. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence systems in clinical routine is still hampered by the necessity of a medical device certification and/or by the difficulty of implementing these systems in a clinic's quality management system. In this context, the key questions for a user are how to ensure robust model predictions and how to appraise the quality of a model's results on a regular basis. In this paper we discuss some conceptual foundation for a clinical implementation of a machine learning system and argue that both vendors and users should take certain responsibilities, as is already common practice for high-risk medical equipment. We propose the methodology from AAPM Task Group 100 report No. 283 as a conceptual framework for developing risk-driven a quality management program for a clinical process that encompasses a machine learning system. This is illustrated with an example of a clinical workflow. Our analysis shows how the risk evaluation in this framework can accommodate artificial intelligence based systems independently of their robustness evaluation or the user's in–house expertise. In particular, we highlight how the degree of interpretability of a machine learning system can be systematically accounted for within the risk evaluation and in the development of a quality management system. [ABSTRACT FROM AUTHOR]

• Novel approach for plastic waste detection with the outperformance than others. • Experiment with the dataset from the real conveyor of plastic waste sorting. • Explanation for the outperformance of fine-tuning DETR compared to other methods. • An end-to-end plastic waste sorting process without hand-designed postprocessing. Every year human discharges about 350 million tons of plastic waste into the environment and can be projected to triple in 2060 without any attempts to change situation. From 1970 to 2019, an estimation of 130 million tons of plastic waste was accumulated into the rivers, lakes and sea, while only 27 % is recycled and utilized. Moreover, waste treatment plants in most places around the world are using out-of-date technology, may pose a threat to the health of the workers. Therefore, it is essential to modernize these systems for protecting human health. This paper proposes fine-tuning DETR, which applies Artificial Intelligent in plastic waste sorting system. Consequently, this study analyzed the applicability of fine-tuning DETR in the domain of plastic waste categorization and its potential drawbacks. For fair experiment and evaluation, model candidates were trained and evaluated on an industrial plastic waste dataset. The fine-tuning DETR outperformed other candidates in the context of critical indicators, from accuracy (25.1 mAP), processing speed (28 FPS) to computational cost (GFLOPs 86). Furthermore, fine-tuning DETR possesses the capability of autonomous operation without requiring human intervention, distinguishing this candidate from other prevalent algorithms. Our research demonstrates that, fine-tuning DETR specifically and Transformer-based algorithms in general, are entirely suitable and hold significant potential for large-scale application in holistic plastic waste sorting systems. [ABSTRACT FROM AUTHOR]

In recent studies, artificial intelligence models have been developed for the prediction of nitrogen oxide emissions from thermal power plants. This research utilizes an artificial intelligence prediction model with more coal input features than boiler features, including volatility, ash content, sulfur content, fixed carbon, total moisture, calorific value, grinding rate, fuel ratio, coal feeding rate, boiler efficiency, total air volume, and excess air volume. The paper delves into the importance analysis of input features for artificial intelligence models. Moreover, feature importance analysis is not only a prerequisite for predicting nitrogen oxide emissions but also a study providing insights into model performance. An artificial neural networks (ANN) regression model is employed to predict nitrogen oxide emissions, and the results demonstrate that the number of feature importance significantly impacts model performance. The best model performance is achieved with eight specific input features. Finally, after the training and validation processes, the ANN model yields the optimal coefficient of determination (R2) value. • Development of an artificial intelligence prediction model for nitrogen oxide emissions from thermal power plants. • A comprehensive set of input features is utilized for emissions prediction. • Importance analysis of input features significantly impacts model performance. • Optimal model performance was achieved with eight specific input features. • The robust coefficient of determination (R2) value demonstrates the accuracy of the ANN model. [ABSTRACT FROM AUTHOR]

Industry 4.0 has brought modernization to the production system through the network integration of the constituent entities which, combined with the evolution of information technology, has enabled an increase in productivity, product quality, optimization of production costs, and product customization to customer needs. Despite the complexity of human thought, artificial intelligence tries to replicate it in algorithms, creating models capable of processing databases with a high volume of information, and generating valuable information for decision making. Within this area, there are subfields, such as Machine Learning and Deep Learning, which, through mathematical models, define patterns to predict output data from known input data. In addition to this type of algorithm, there are metaheuristic models capable of optimizing the parameters required in Machine Learning and Deep Learning algorithms. These intelligent systems have applications in various areas such as industry, construction, health, logistics processes, and maintenance management, among others. This paper focuses on Artificial Intelligence models addressing Industry 4.0 approach. [ABSTRACT FROM AUTHOR]

Lung cancer is the 2nd most diagnosed cancer worldwide. The corresponding histopathological analysis, being both costly and time-consuming, demands the commitment of skilled professionals who, while engrossed in this task, experience constraints on their ability to attend to other crucial responsibilities. Moreover, as it is a human-driven process, mistakes may lead to incorrect diagnosis and treatment. Given the disease frequency and mortality, automated diagnostic systems, using Artificial Intelligence (AI), represent valuable improvements in diagnostic timing and overall performance. Recently, Deep Learning (DL) has been widely used for extracting features from histopathologic images approaching more accurate and expeditious analysis. With this line of research in mind, a brief review of recent technical/scientific works within the scope of lung Digital Pathology (DP) image analysis is provided in this paper, covering different computer vision tasks including classification, segmentation, and detection. Furthermore, available datasets and open-source annotation tools capable of providing support to the aforementioned DP-related tasks are also overviewed. Afterward, a summary table and a discussion around the reviewed approaches is provided, consolidating critical information such as technique/DL architecture, involved datasets, metrics, etc. From this study, it was observed that the ARA-CNN technique achieved the highest Area Under the Curve (AUC) ranging from 0.72 to 0.99 for classification. On the other hand, the multimodal-based approach, with an AUC of 0.95, performed better for the segmentation task. As for the detection task, the BCNN approach stood out, achieving a high AUC of 0.988. This review work aims to provide a comprehensive overview of recent advancements in lung DP image analysis and serves as a foundation for future research in this critical area. [ABSTRACT FROM AUTHOR]

Enterprise Information Systems (EIS) comprise components for decision-making in an organization. While traditional Decision Support Systems (DSS) rely on sophistically designed decision models, this approach has its limitations when it comes to making decisions based on large amounts of unstructured data. In this paper, we present the case of online community management, where moderators need to decide if user content (i.e., comments) can be published. We have implemented a moderation platform that utilizes Natural Language Processing (NLP) and Machine Learning (ML) to support moderators in their decision-making. From the development process and adoption of our platform, which were carried out as a Design Science Research (DSR) project, we have derived six design principles that assist in designing ML-based DSS. Our results imply that an ML-based DSS should be implemented using an open, customizable system, where decisions are made transparent and interpretable to users. Users need special onboarding and should always have the possibility to overrule the system. [ABSTRACT FROM AUTHOR]

Video consultations and telemedicine play a key role in healthcare. It is crucial to assist institutions in identifying which patients are more likely to use them, increasing the adoption rate. Even though Artificial Intelligence models are important, certain cases require a human centric approach to inform patients and doctors, ensuring trust in the tools provided. This can be achieved through interpretable Machine Learning models and Statistical Analysis. In this paper we show that even though the accuracy of the Machine Learning models used (Decision Trees, Logistic Regression, and Random Forest) was between 84%-89% on the training set and 69%-76% on the test set the models lacked good ability to identify the users. Afterwards, additional statistical analysis evidenced that the main driver to use video consultations in a sample of participations with a higher-than-average education is the presence of one or more chronic diseases. [ABSTRACT FROM AUTHOR]

Artificial Intelligence systems have been increasingly developing, demonstrating new forms of implementation and utilization, being employed in many sectors. In this regard, aiming to keep up with social development and enhance the development of procedural acts, the Brazilian Judiciary System has been seeking to implement new forms of technology over the years. Although gradually, an evolution can be observed in the sector, such as options that circumvent the use of physical documents and a departure from conducting all acts in person. This has led to the implementation and development of Artificial Intelligence systems within the judiciary, aiming to further improve the Brazilian judicial system. Therefore, this present work seeks to provide a historical overview of the advancements that have occurred in the Brazilian Judiciary System regarding its technological evolution, describing the initial modifications as well as the current applications of Artificial Intelligence. Additionally, it presents their objectives and capabilities. Thus, in conclusion, this paper demonstrates that the progress made over the years is immeasurable, and the application of Artificial Intelligence systems in the Brazilian Judiciary System is already becoming consolidated, with the potential for even greater growth in the coming years. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is an important enabler of the fourth and fifth industrial revolutions. It has the potential to empower intelligent production systems that are central to industry 4.0 and help place worker wellbeing and the planet at the centre of industry 5.0 processes. However, few studies have considered the different roles of AI in industry 4.0 and 5.0. This paper reports on a bibliometric review of 2887 documents examining the use of AI for industry 4.0 and industry 5.0. We found that scholarly activity increased rapidly from 2018 onwards and can be attributed to a large variety of countries, including India, South Africa and China. Our analysis reveals four clusters in the literature, with two showing particular promise for researchers notably around the themes of industry 5.0 and deep learning. Further insights are presented and discussed, followed by a future research agenda. [ABSTRACT FROM AUTHOR]

With the ever increasing number of active satellites in space, the rising demand for larger formations of small satellites and the commercialization of the space industry (so-called New Space), the realization of manufacturing processes in orbit comes closer to reality. Reducing launch costs and risks, allowing for faster on-demand deployment of individually configured satellites as well as the prospect for possible on-orbit servicing for satellites makes the idea of realizing an in-orbit factory promising. In this paper, we present a novel approach to an in-orbit factory of small satellites covering a digital process twin, AI-based fault detection, and teleoperated robot-control, which are being researched as part of the "AI-enabled Cyber–Physical In-Orbit Factory" project. In addition to the integration of modern automation and Industry 4.0 production approaches, the question of how artificial intelligence (AI) and learning approaches can be used to make the production process more robust, fault-tolerant and autonomous is addressed. This lays the foundation for a later realization of satellite production in space in the form of an in-orbit factory. Central aspect is the development of a robotic AIT (Assembly, Integration and Testing) system where a small satellite could be assembled by a manipulator robot from modular subsystems. Approaches developed to improving this production process with AI include employing neural networks for optical and electrical fault detection of components. Force sensitive measuring and motion training helps to deal with uncertainties and tolerances during assembly. An AI-guided teleoperated control of the robot arm allows for human intervention while a Digital Process Twin represents process data and provides supervision during the whole production process. Approaches and results towards automated satellite production are presented in detail. • An automated production system for modular small satellites is proposed. • Central element is a force-sensitive robotic assembly, integration & testing system. • AI and machine learning approaches are applied to make the process more robust. • A digital process twin supervises the autonomous production. • A multi-modal shared control approach allows human teleoperation if required. [ABSTRACT FROM AUTHOR]

Artificial Intelligence has found wide application, but also poses risks due to unintentional or malicious tampering during deployment. Regular checks are therefore necessary to detect and prevent such risks. Fragile watermarking is a technique used to identify tampering in AI models. However, previous methods have faced challenges including risks of omission, additional information transmission, and inability to locate tampering precisely. In this paper, we propose a method for detecting tampered parameters and bits, which can be used to detect, locate, and restore parameters that have been tampered with. We also propose an adaptive embedding method that maximizes information capacity while maintaining model accuracy. Our approach was tested on multiple neural networks subjected to attacks that modified weight parameters, and our results demonstrate that our method achieved great recovery performance when the modification rate was below 20%. Furthermore, for models where watermarking significantly affected accuracy, we utilized an adaptive bit technique to recover more than 15% of the accuracy loss of the model. • Fragile watermarking for rigorous neural network integrity detection. • Watermarking ensures 100% tampering detection, precise location, and recovery. • Watermarking fragility by combining Mutual- and Self-neural net parameters. • Incorporates watermarking with neural net feature, maintaining watermarked models' performance via gradient descent. • Bit-level training controls step size with carefully set hyperparameters. [ABSTRACT FROM AUTHOR]

In the aging global society, heart failure and valvular heart diseases, including aortic stenosis, are affecting millions of people and healthcare systems worldwide. Although the number of effective treatment options has increased in recent years, the lack of effective screening methods is provoking continued high mortality and rehospitalization rates. Appropriately, auscultation has been the primary option for screening such patients, however, challenges arise due to the variability in auscultation skills, the objectivity of the clinical method, and the presence of sounds inaudible to the human ear. To address challenges associated with the current approach towards auscultation, the hardware of Super StethoScope was developed. This paper is composed of (1) a background literature review of bioacoustic research regarding heart disease detection, (2) an introduction of our approach to heart sound research and development of Super StethoScope, (3) a discussion of the application of remote auscultation to telemedicine, and (4) results of a market needs survey on traditional and remote auscultation. Heart sounds and murmurs, if collected properly, have been shown to closely represent heart disease characteristics. Correspondingly, the main characteristics of Super StethoScope include: (1) simultaneous collection of electrocardiographic and heart sound for the detection of heart rate variability, (2) optimized signal-to-noise ratio in the audible frequency bands, and (3) acquisition of heart sounds including the inaudible frequency ranges. Due to the ability to visualize the data, the device is able to provide quantitative results without disturbance by sound quality alterations during remote auscultations. An online survey of 3648 doctors confirmed that auscultation is the common examination method used in today's clinical practice and revealed that artificial intelligence-based heart sound analysis systems are expected to be integrated into clinicians' practices. Super StethoScope would open new horizons for heart sound research and telemedicine. [Display omitted] • Together, phonocardiograms and electrocardiograms improve heart disease screening. • Collecting signals from the crucial frequency band of heart sounds is challenging. • Quality of sounds and clinical data are the key factors of artificial intelligence as a medical device. • Biomarkers derived from heart sounds enable accessible and noninvasive diagnoses. • Heart sound analysis provides new operational values in telemedicine and education. [ABSTRACT FROM AUTHOR]

Forming processes are known for their intricacies in prediction and control due to the complex loading conditions and material flow. This paper will first introduce the AI algorithms used or having potential to be used in forming, and then investigate the state-of-the-art advances of AI-based technologies in forming processes with four main pillars of process simulation, process design and optimization, in-situ process control, and qualification and certification of forming processes and formed products. Future directions of AI in forming for both academic research and industrial applications will be proposed to leverage digitalization and data science to explore new solutions in forming processes. [ABSTRACT FROM AUTHOR]

• To use AI chatbots in nursing, it is important to know how they work with nursing theories. This study provides a guideline on how to use GPT 4.0 with prompt learning in nursing, based on Benner's Theory. • It gives insights on bias, best practices for using ChatGPT in nursing education, and how AI can improve nursing skills such as problem-solving and clinical decision-making. • A case study shows how ChatGPT can be used in nursing education and addresses challenges and opportunities found in other studies. Artificial intelligence (AI) and large language models, such as ChatGPT, have the potential to enhance nursing education by serving as a virtual mentor who can provide real-time guidance and resources. However, integrating AI chatbots into nursing necessitates a detailed understanding of how to align these technologies with established nursing theories. To develop guidelines on how to employ GPT 4.0 using the prompt learning method. In the method of prompt learning, users give specific prompts, and the AI model reacts according to its training, specifically aligned with the progression of knowledge and skills in nursing education as outlined by Benner's theory. This study used a case study methodology. The customization of a conversational AI chatbot is shown to support the development of nursing knowledge and skills. This paper outlines how to integrate Benner's theory with ChatGPT's capabilities, addresses bias issues, and establishes best practices for the safe and effective use of AI in nursing. The findings have important implications for the advancement of nursing education and the safe and responsible use of AI tools in clinical environments. [ABSTRACT FROM AUTHOR]

Virtual reality's (VR) applications in education have been a topic of interest to the academia in recent years. Although literature expands significantly worldwide, the focus on education still presents a high number of opportunities to explore. In Latin America, the literature regarding the use of virtual reality is limited and not yet systematized. Therefore, the present study aims to provide an overview of Latin America's research publications regarding the use of VR in higher education. Using bibliometric measures and coding in Scopus and Web of Science (WoS) databases' outputs, we analyzed 126 papers, the majority of them from social sciences, followed by engineering, business, and medicine. Content tree analysis resulted in 5 dimensions: 'Functional aspects of using VR as a learning tool', 'Teaching strategies and methodologies using VR', 'Behavioral aspects of using VR as a learning tool', 'Outputs of VR use as an educational tool' and 'Competencies and readiness to use VR as a learning tool'. Most publications (74,6%) focus or present as one of the discussions the functional aspects of VR, such as access, viability, usefulness, virtual environment, the association with artificial intelligence, among others. [ABSTRACT FROM AUTHOR]

Modern ransomware implement innovative techniques and tactics to bypass existing security measures. Hence, predicting and forecasting such kind of malware is crucial for enhancing the overall cybersecurity posture in an increasingly digital and interconnected world. In this paper, we propose a proactive approach for predicting ransomware attacks. We integrated a dynamic deep learning algorithm for analyzing memory-based features. This allows us to detect the existence of ransomware indicators regardless of the usage of obfuscation techniques, such as code encryption. Thus, offering advantages in terms of obfuscation resistance, realtime insights threat analysis and adaptability to evolving ransomware threats landscape. Experimental results using recent datasets demonstrate the effectiveness of the proposed approach in identifying modern ransomware samples with a weighted average of 99.98%, 99.96%, 100%, and 99.98% for accuracy, precision, recall and fl-score respectively. [ABSTRACT FROM AUTHOR]

This research leverages the potential of digital twin technology to present a novel method of predictive maintenance for 6G communication networks. With the increasing need for 6G networks to be consistently reliable and always available, this essay emphasises the need for more advanced maintenance techniques. The seamless integration of digital twin technology makes it possible to monitor, analyse, and simulate the 6G network in real time. This innovation makes it easier to use artificial intelligence to fine-tune maintenance schedules and anticipate maintenance requirements in advance. The paper fervently promotes the use of digital twin technology as a vital instrument for raising the bar for maintenance requirements in the context of 6G networks. [ABSTRACT FROM AUTHOR]

In an era dominated by the intersection of advanced technology and traditional industries, the domain of agriculture is on the verge of a revolutionary transformation. This article introduces a solution for vineyard producers, harnessing satellite imagery, weather data, and deep learning (DL) to identify vineyard diseases robustly. This solution, designed for proactive plant health management, stands as a transformative tool towards digital viticulture. Such tools transition from luxuries to essentials as vineyards confront evolving challenges like climate change and new pathogens. Our research builds on the hypothesis that customising deep learning architectures for specific tasks is crucial in enhancing their effectiveness. We contribute by introducing a tailored convolutional neural network (CNN) architecture, developed specifically for the classification of plant diseases using vineyard imagery. The experimental results demonstrate that our custom CNN architecture exhibits performance on par with established state-of-the-art models like ResNet50 and MobileNetV2, underscoring the value of specialized solutions in addressing the unique challenges of viticulture. This paper introduces an overview of the solution's architecture, presents the implementation of DL modules with their corresponding results, and describes use case scenarios. [ABSTRACT FROM AUTHOR]

In the digital era, recommender systems (RS) have become an integral part of our daily interactions, exerting a significant impact on users and society. However, this also raises ethical challenges related to RS that should be considered. Addressing these challenges requires the application of explainable artificial intelligence (XAI) models to make RS more understandable. Based on the current state-of-the-art literature, this paper aims to provide a comprehensive overview of XAI for RS and its ethical implications, with the aim of proposing a research agenda for ethical RS based on XAI. The findings of the literature review show that neural network-based RS have received much attention in terms of offering explanations, while there is a research gap in explaining context-based RS and in evaluating explanations. In addition, a set of ethical challenges for RS are discussed by exploring how explanations for recommendations can contribute to the ethical use of RS. [ABSTRACT FROM AUTHOR]

Augmented Reality has become a bridge from the virtual to the real world. This technology has been an implementable choice for fashion companies to provide more information and customization for their user's shopping experience. From the COVID-19 pandemic alongside technological globalization, fashion industries have to find a new way for customers to purchase products. All things considered, AR helped fashion industries keep customers attracted and satisfied. This paper is conducted in a Systematic Literature Review manner to review and compare the AR technologies used by fashion industries. Furthermore, the result would be a proposition of which Augmented Reality technology or platform option is feasible and implementable for the fashion industry. After comparing the technologies used in past papers and their technical and economic feasibility, we propose a 3-term plan. The short-term plan with zero cost and mobile platform-focused application, the medium-term plan with an upgraded version of the garment object from 2D images to 3D objects, and the long-term plan changing the platform used from mobile to a new device. We also conclude that artificial intelligence can also support and improve the augmented reality experience for customers in the fashion retail industry by implementing an AI-powered virtual assistant. [ABSTRACT FROM AUTHOR]