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The use of Artificial Intelligence (AI) to preserve and promote cultural heritage has experienced significant growth in recent years. Among the various areas of cultural heritage, numismatics have emerged as a particularly promising field where we can develop AI solutions. Numismatics refers to the study of coins, tokens, paper money, and medals, which play a critical role in understanding human history and culture. However, there are still limited resources available to help researchers and collectors in the identification of coins. This is due to the vast number of coins in circulation, which presents a significant challenge in developing smart tools for classification tasks. This paper aims to provide a contribution to this setting. In particular, we start by creating a new dataset called EURO-Coin, which consists of images showing the side of coins with reliefs and is designed to facilitate the training and testing of AI models for euro coin classification. Then, we propose two approaches that leverage Convolutional Neural Networks and self-attention layers to classify the country and value of the coins. In our experiments, we obtain an accuracy of 86.9% for country classification and an accuracy of 96.4% for value classification. Finally, we conduct an ablation study to evaluate the impact of the preprocessing activities and attention layers in our approaches. [ABSTRACT FROM AUTHOR]

Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis. Key Points: • AI systems need to ensure that the benefits of biopsy avoidance are delivered with consistent high specificities, at a range of disease prevalence. • Initial work has focused on developing systems as diagnostic supporting aids for outlining tasks, so they can be integrated into the radiologists' workflow to support MRI-directed biopsies. • Decision support tools require a larger body of work including multicentric, multivendor studies where the clinical needs, disease prevalence, patient preferences, and clinical setting are additionally defined. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) in ophthalmology has promoted the development of the discipline, offering opportunities for enhancing diagnostic accuracy, patient care, and treatment outcomes. This paper aims to provide a foundational understanding of AI applications in ophthalmology, with a focus on interpreting studies related to AI-driven diagnostics. The core of our discussion is to explore various AI methods, including deep learning (DL) frameworks for detecting and quantifying ophthalmic features in imaging data, as well as using transfer learning for effective model training in limited datasets. The paper highlights the importance of high-quality, diverse datasets for training AI models and the need for transparent reporting of methodologies to ensure reproducibility and reliability in AI studies. Furthermore, we address the clinical implications of AI diagnostics, emphasizing the balance between minimizing false negatives to avoid missed diagnoses and reducing false positives to prevent unnecessary interventions. The paper also discusses the ethical considerations and potential biases in AI models, underscoring the importance of continuous monitoring and improvement of AI systems in clinical settings. In conclusion, this paper serves as a primer for ophthalmologists seeking to understand the basics of AI in their field, guiding them through the critical aspects of interpreting AI studies and the practical considerations for integrating AI into clinical practice. [ABSTRACT FROM AUTHOR]

Gynecological health remains a critical aspect of women's overall well-being, with profound implications for maternal and reproductive outcomes. This comprehensive review synthesizes the current state of knowledge on four pivotal aspects of gynecological health: preterm birth, breast cancer and cervical cancer and infertility treatment. Machine learning (ML) has emerged as a transformative technology with the potential to revolutionize gynecology and women's healthcare. The subsets of AI, namely, machine learning (ML) and deep learning (DL) methods, have aided in detecting complex patterns from huge datasets and using such patterns in making predictions. This paper investigates how machine learning (ML) algorithms are employed in the field of gynecology to tackle crucial issues pertaining to women's health. This paper also investigates the integration of ultrasound technology with artificial intelligence (AI) during the initial, intermediate, and final stages of pregnancy. Additionally, it delves into the diverse applications of AI throughout each trimester. This review paper provides an overview of machine learning (ML) models, introduces natural language processing (NLP) concepts, including ChatGPT, and discusses the clinical applications of artificial intelligence (AI) in gynecology. Additionally, the paper outlines the challenges in utilizing machine learning within the field of gynecology. [ABSTRACT FROM AUTHOR]

This paper introduces the collection of the Journal on Machine Learning (ML) and the user. It provides a brief history of ML from the 1950's through to the current time, sketching the nature of the kinds of precursor AI techniques used in such things as expert systems right the way through to the emergence of ML and its tool sets, including deep learning. It concludes with the 'generative AI' used in such ML technologies as PaLM and GPT-3. The history highlights key changes and developments in ML, the especial importance and limitations of deep learning, and the changing attitudes and expectations of users in an environment when ML can and often is oversold. The paper then explores the ways CSCW research has addressed the social context of organisational systems and how the same can apply for ML tools and techniques. It urges research that focuses on the particular ways that ML comes to fit into 'real world' collaborative work sites and hence speaks to the CSCW cannon. [ABSTRACT FROM AUTHOR]

De-mining operations are of critical importance for humanitarian efforts and safety in conflict-affected regions. In this paper, we address the challenge of enhancing the accuracy and efficiency of mine detection systems. We present an innovative Deep Learning architecture tailored for pulse induction-based Metallic Mine Detectors (MMD), so called DL-MMD. Our methodology leverages deep neural networks to distinguish amongst nine distinct materials with an exceptional validation accuracy of 93.5%. This high level of precision enables us not only to differentiate between anti-personnel mines, without metal plates but also to detect minuscule 0.2-g vertical paper pins in both mineralized soil and non-mineralized environments. Moreover, through comparative analysis, we demonstrate a substantial 3% and 7% improvement (approx.) in accuracy performance compared to the traditional K-Nearest Neighbors and Support Vector Machine classifiers, respectively. The fusion of deep neural networks with the pulse induction-based MMD not only presents a cost-effective solution but also significantly expedites decision-making processes in de-mining operations, ultimately contributing to improved safety and effectiveness in these critical endeavors. [ABSTRACT FROM AUTHOR]

The increasing availability of hydrological and physiographic spatiotemporal data has boosted machine learning's role in rapid flood mapping. Yet, data scarcity, especially high-resolution DEMs, challenges regions with limited access. This paper examines how DEM type and resolution affect flood prediction accuracy, utilizing a cutting-edge deep learning (DL) method called 1D convolutional neural network (CNN). It utilizes synthetic hydrographs as training input and water depth data obtained from LISFLOOD-FP, a 2D hydrodynamic model, as target data. This study investigates digital surface models (DSMs) and digital terrain models (DTMs) derived from a 1 m LIDAR-based DTM, with resolutions from 15 to 30 m. The methodology is applied and assessed in a established benchmark, city of Carlisle, UK. The models' performance is then evaluated and compared against an observed flood event using RMSE, Bias, and Fit indices. Leveraging the insights gained from this region, the paper discusses the applicability of the methodology to address the challenges encountered in a data-scarce flood-prone region, exemplified by Pakistan. Results indicated that utilizing a 30 m DTM outperformed a 30 m DSM in terms of flood depth prediction accuracy by about 21% during the flood peak stage, highlighting the superior performance of DTM at lower resolutions. Increasing the resolution of DTM to 15 m resulted in a minimum 50% increase in RMSE and a 20% increase in fit index across all flood stages. The findings emphasize that while a coarser resolution DEM may impact the accuracy of machine learning models, it remains a viable option for rapid flood prediction. However, even a slight improvement in data resolution in data-scarce regions would provide significant added value, ultimately enhancing flood risk management. [ABSTRACT FROM AUTHOR]

In response to the personalized needs of the tourism industry, intelligent tourism planning services should be adopted. Through artificial intelligence and Big data technology, users' travel preferences, time, budget and other information are analyzed and mined in depth to provide users with more suitable travel routes and services. Therefore, according to the demand of tourist path recommendation in cultural scenic spots, this paper proposes a light sensor positioning method based on deep learning algorithm. This paper expounds the advantages of using optical sensor positioning for path recommendation, and describes the specific steps and processes of the optical sensor positioning method based on deep learning algorithm in detail. Finally, the feasibility and effect of this method are verified by experiments and data analysis. The results show that the localization of light sensor based on deep learning algorithm has high accuracy and practicability in the travel path recommendation of cultural scenic spots. Therefore, this method can provide accurate and reasonable path recommendation for tourists in cultural scenic spots and improve tourism experience and benefits. With the continuous development of the tourism market and the changing needs of users, this personalized tourism route planning and recommendation system will be increasingly widely applied and promoted. [ABSTRACT FROM AUTHOR]

Early detection of the adenocarcinoma cancer in colon tissue by means of explainable deep learning, by classifying histological images and providing visual explainability on model prediction. Considering that in recent years, deep learning techniques have emerged as powerful techniques in medical image analysis, offering unprecedented accuracy and efficiency, in this paper we propose a method to automatically detect the presence of cancerous cells in colon tissue images. Various deep learning architectures are considered, with the aim of considering the best one in terms of quantitative and qualitative results. As a matter of fact, we consider qualitative results by taking into account the so-called prediction explainability, by providing a way to highlight on the tissue images the areas that from the model point of view are related to the presence of colon cancer. The experimental analysis, performed on 10,000 colon issue images, showed the effectiveness of the proposed method by obtaining an accuracy equal to 0.99. The experimental analysis shows that the proposed method can be successfully exploited for colon cancer detection and localisation from tissue images. [ABSTRACT FROM AUTHOR]

The illicit traffic of cultural goods remains a persistent global challenge, despite the proliferation of comprehensive legislative frameworks developed to address and prevent cultural property crimes. Online platforms, especially social media and e-commerce, have facilitated illegal trade and pose significant challenges for law enforcement agencies. To address this issue, the European project SIGNIFICANCE was born, with the aim of combating illicit traffic of Cultural Heritage (CH) goods. This paper presents the outcomes of the project, introducing a user-friendly platform that employs Artificial Intelligence (AI) and Deep learning (DL) to prevent and combat illicit activities. The platform enables authorities to identify, track, and block illegal activities in the online domain, thereby aiding successful prosecutions of criminal networks. Moreover, it incorporates an ontology-based approach, providing comprehensive information on the cultural significance, provenance, and legal status of identified artefacts. This enables users to access valuable contextual information during the scraping and classification phases, facilitating informed decision-making and targeted actions. To accomplish these objectives, computationally intensive tasks are executed on the HPC CyClone infrastructure, optimizing computing resources, time, and cost efficiency. Notably, the infrastructure supports algorithm modelling and training, as well as web, dark web and social media scraping and data classification. Preliminary results indicate a 10–15% increase in the identification of illicit artifacts, demonstrating the platform's effectiveness in enhancing law enforcement capabilities. [ABSTRACT FROM AUTHOR]

Lung cancer is an uncontrolled growth of tissue causing a lump in the human lung. If lung cancer can be detected early, it can increase the survival rate. Therefore, a multi-classification approach of lung nodule detection with high computational effectiveness is required. In this paper, a multi-classification approach of lung nodule detection and classification is proposed using artificial intelligence on computed tomography (CT) scan images. Different preprocessing steps are applied for resizing, smoothing, and enhancement of the CT images. Then, two different approaches for feature extraction using VGG16 transfer learning and morphological segmentation are proposed. Morphological segmentation and feature extraction are applied for the segmentation of the region of interest and to extract the distinct features. Finally, the proposed deep learning architecture and seven different machine learning algorithms are applied on the preprocessed data and the extracted features for the classification of lung nodules into three classes: malignant, benign, and normal. It is observed that the stacked ensemble model of deep learning convolutional neural network (CNN) and VGG16 transfer learning models (CNN+VGG16) can achieve 99.55% accuracy using preprocessed data. It is also observed that all the ML algorithms perform with reasonably high accuracy using the low-dimensional morphological features. It is observed from the fivefold cross-validation results that logistic regression performs with 99.36% accuracy in 23.71 s time using the preprocessed data. Whereas, using the morphological features, k-nearest neighbor, and the support vector machine perform with the highest accuracy of 99.76% with very reduced computational time of 0.017 and 0.008 s, respectively. [ABSTRACT FROM AUTHOR]

Visual XAI methods enable experts to reveal importance maps highlighting intended classes over input images. This research paper presents a novel approach to visual explainable artificial intelligence (XAI) for object detection in deep learning models. The study investigates the effectiveness of activation maps generated by five different methods, namely GradCAM, GradCAM++, EigenCAM, HayCAM, and a newly proposed method called "HayCAMJ", in detecting objects within images. The experiments were conducted on two datasets (Pascal VOC 2007 and Pascal VOC 2012) and three models (ResNet18, ResNet34, and MobileNet). Zero padding was applied to resize and center the objects due to the large objects in the images. The results show that HayCAMJ performs better than other XAI techniques in detecting small objects. This finding suggests that HayCAMJ has the potential to become a promising new approach for object detection in deep classification models. [ABSTRACT FROM AUTHOR]

Over the preceding decade, there has been a discernible surge in the prominence of artificial intelligence, marked by the development of various methodologies, among which deep learning emerges as a particularly auspicious technique. The captivating attribute of deep learning, characterised by its capacity to glean intricate feature representations from data, has served as a catalyst for pioneering approaches and methodologies spanning a multitude of domains. In the face of the burgeoning exponential growth in digital medical image data, the exigency for adept image compression methodologies has become increasingly pronounced. These methodologies are designed to preserve bandwidth and storage resources, thereby ensuring the seamless and efficient transmission of data within medical applications. The critical nature of medical image compression accentuates the imperative to confront the challenges precipitated by the escalating deluge of medical image data. This review paper undertakes a comprehensive examination of medical image compression, with a predominant focus on sophisticated, research-driven deep learning techniques. It delves into a spectrum of approaches, encompassing the amalgamation of deep learning with conventional compression algorithms and the application of deep learning to enhance compression quality. Additionally, the review endeavours to explicate these fundamental concepts, elucidating their inherent characteristics, merits, and limitations. [ABSTRACT FROM AUTHOR]

In the globalization trend, China's cultural heritage is in danger of gradually disappearing. The protection and inheritance of these precious cultural resources has become a critical task. This paper focuses on the Miao batik culture in Guizhou Province, China, and explores the application of knowledge graphs, natural language processing, and deep learning techniques in the promotion and protection of batik culture. We propose a dual-channel mechanism that integrates semantic and visual information, aiming to connect batik pattern features with cultural connotations. First, we use natural language processing techniques to automatically extract batik-related entities and relationships from the literature, and construct and visualize a structured batik pattern knowledge graph. Based on this knowledge graph, users can textually search and understand the images, meanings, taboos, and other cultural information of specific patterns. Second, for the batik pattern classification, we propose an improved ResNet34 model. By embedding average pooling and convolutional operations into the residual blocks and introducing long-range residual connections, the classification performance is enhanced. By inputting pattern images into this model, their categories can be accurately identified, and then the underlying cultural connotations can be understood. Experimental results show that our model outperforms other mainstream models in evaluation metrics such as accuracy, precision, recall, and F1-score, achieving 94.46%, 94.47%, 93.62%, and 93.8%, respectively. This research provides new ideas for the digital protection of batik culture and demonstrates the great potential of artificial intelligence technology in cultural heritage protection. [ABSTRACT FROM AUTHOR]

Deep learning (LeCun et al. in Nature 521(7553):436–444, 2015) workloads are common in today's production clusters due to the proliferation of deep learning driven AI (Russell and Norvig, Artificial intelligence: a modern approach, Pearson Education, Upper Saddle River, 2003) services. As deep learning workloads and GPUs all grow more heterogeneous, efficient and fair resource scheduling are the key to the maximal performance of a deep learning cluster. However, existing cluster schedulers are largely not tailored to deep learning jobs, and typically specifying a fixed amount of resources for each job, prohibiting high resource efficiency and job performance. Thus we reconsider the problem in this paper, and to resolve them we proposes MixTran, which works fairly and efficiently for mixed deep learning workloads on heterogeneous GPU clusters. At first, MixTran abstracts heterogeneous GPU resources and distributes them fairly to users in the form of virtual tickets. Then, MixTran transfers uniform requests into a global optimization model, which can make efficient use of GPUs and satisfy the quantified resource requests, heterogeneous node constraints and user fairness constraints. Finally, MixTran conducts greedy resource trading according to the second trader price, which can benefit both users. In the evaluation, we prove MixTran can greatly decrease total execution time of deep learning workloads than traditional scheduler up to 30%–50% and maintain the fairness of multiple users. [ABSTRACT FROM AUTHOR]

Fairness has become a critical value online, and the latest studies consider it in many problems. In recommender systems, fairness is important since the visibility of items is controlled by systems. Previous fairness-aware recommender systems assume that sufficient relationship data between users and items are available. However, it is common that new users and items are frequently introduced, and they have no relationship data yet. In this paper, we study recommendation methods to enhance fairness in a cold-start state. Fairness is more significant when the preference of a user or the popularity of an item is unknown. We propose a meta-learning-based cold-start recommendation framework called FaRM to alleviate the unfairness of recommendations. The proposed framework consists of three steps. We first propose a fairness-aware meta-path generation method to eliminate bias in sensitive attributes. In addition, we construct fairness-aware user representations through the meta-path aggregation approach. Then, we propose a novel fairness objective function and introduce a joint learning method to minimize the trade-off between relevancy and fairness. In extensive experiments with various cold-start scenarios, it is shown that FaRM is significantly superior in fairness performance while preserving relevance accuracy over previous work. [ABSTRACT FROM AUTHOR]

The emergence of data-driven analytics and intelligent monitoring systems is radically transforming the world of competitive sports. However, robust real-time activity recognition remains elusive for rapid-paced sports like table tennis due to the complexity of strokes and quick maneuvering. This paper tries to overcome this challenge through an innovative cloud-supported system integrating the Internet of Things (IoT), machine learning, and wearable sensors for automated analysis of table tennis gameplay. We strategically set up a multi-camera IoT system around a table tennis court to compile a rich labeled image dataset encapsulating over 15,000 frames. To improve model generalization, the images captured various playing styles, lighting conditions, and camera angles. We developed tailored SVM and CNN architectures optimized for table tennis activity classification. The models were trained on GPU-accelerated platforms using the curated dataset. After hyperparameter tuning and cross-validation, the CNN model achieved 96.2% accuracy, a precision value of 96.3%, a recall value of 95.2%, and an F1-score of 96.3% in classifying standard strokes and serves. Additionally, the models exhibited impressive processing speeds of 22–34 fps, enabling real-time utilization. Our proposed model outperformed the latest models in this field with higher accuracy, recall values, F1-score, and precision. While expanding the dataset diversity and testing variations of deep network architectures could further enhance performance, this paper demonstrates a crucial leap toward helping real-time analytics in table tennis using an AI-powered computer vision approach. The hybrid system combining IoT, wearables, and machine learning establishes a framework to transform data into actionable and timely insights for table tennis players and coaches. [ABSTRACT FROM AUTHOR]

The emergence of Artificial Intelligence (AI) provides brand-new technical support and a broader resource platform for college oral English teaching and evaluation. The advancement of science, technology, and networks has resulted in significant educational changes. Because of these, the demand for English instruction is rising quickly, along with the level of cultural integration. However, due to the limits of conventional teaching techniques and the slow expansion of information technology, oral English classes have been discarded and discontinued for a long time. Based on the preceding, this paper first addresses the shortcomings of traditional college-level spoken English instruction by exposing content and timing deficiencies. It then describes how spoken English data are collected, preprocessed, and framed before being standardized for consistency. After that, this paper uses mapping recognition results using a DBN speech model and correlation coefficients in the evaluation. In addition, it uses the Pairwise Variability Index in rhythm evaluation to assess stress distribution differences between test and standard pronunciation. Besides the above, the proposed approach uses Support Vector Machine (SVM) optimization inside statistical learning to improve phoneme recognition and reliability, especially when dealing with difficult-to-distinguish phoneme sets. Further analysis is carried out using neural networks, which include excitation functions and error computations. Finally, the complete design emphasizes user-centric functional characteristics such as practicability, user needs, and robust system management subsystems. Experiments suggest that the natural language processing-based oral English teaching mode can increase students' overall oral English skills. These results reveal that students' excitement increased by 33.3%, their verbal fluency increased by 86%, and their vocabulary learning increased by 16.1%. The results show that this method can effectively assist students in learning oral English. [ABSTRACT FROM AUTHOR]

Currently, much of the world's IVF data is stuck in paper charts, owned and sold by private IVF clinics, with images taken on different microscopes, and cropped any number of ways to remove patient data. Image data (if collected at all) is almost never connected to the IVF lab KPI data, which is almost never connected to the clinical data. Artificial intelligence (AI) - systems that program themselves to listen, think, predict, anticipate, prognose, diagnose, and RESPOND better than people - are the future of reproductive medicine. [Extracted from the article]

Deep learning integration in cancer diagnosis enhances accuracy and diagnosis speed which helps clinical decision-making and improves health outcomes. Despite all these benefits in cancer diagnosis, the present AI models in urology cancer diagnosis have not been sufficiently reviewed systematically. This paper reviews the artificial intelligence approaches used in cancer diagnosis, prediction, and treatment of urology cancer. AI models and their applications in urology subspecialties are evaluated and discussed. The Scopus, Microsoft Academic and PubMed/MEDLINE databases were searched in November 2022 using the terms "artificial intelligence", "neural network", "machine learning," or "deep learning" combined with the phrase "urology cancers". The search was limited to publications published within the previous 20 years to identify cutting-edge deep-learning applications published in English. Irrelevant review articles and publications were eliminated. The included research involves two kinds of research analysis: quantitative and qualitative. 48 articles were included in this survey. 25 studies proposed several approaches for prostate cancers, while 15 were for bladder cancers. 8 studies discussed renal cell carcinoma and kidney cancer. The models presented to detect urology cancers have achieved high detection accuracy (77–95%). Deep learning approaches that use convolutional neural networks have achieved the highest accuracy among other techniques. Although it is still progressing, the development of AI models for urology cancer detection, prediction, and therapy has shown significant promise. Additional research is required to employ more extensive, higher-quality, and more recent datasets to the clinical performance of the proposed AI models in urology cancer applications. [ABSTRACT FROM AUTHOR]

An artificial neural network (ANN) in information technology is a system of hardware or software modeled after the operation of neurons in the human brain. ANNs, often known as "neural networks," are a form of deep learning technology that falls under the umbrella of Artificial Intelligence (AI). Commercial applications of these technologies typically focus on optimization and solving complex signal processing and pattern recognition problems. Multiple types of optimization techniques are utilized to determine the optimal neural network for a model. These procedures help determine and define the model's accuracy, dependability, functionality, and capacity. The convergence of the neural network helps determine the number of training iterations required to generate the fewest errors. In this paper, we investigate an activation function to help reduce the training time of the analog-to-digital converter (ADC). A new Hopfield ADC model is proposed by using the proteretic activation function property. We supported our research by simulating the new ADC converter and comparing the traditional Hopfield ADC, the hysteretic ADC, and the proteretic ADC. Experiment and simulation demonstrate that the proteretic function provides a faster rate of convergence than other functions, thereby enhancing the performance of the ADC application. [ABSTRACT FROM AUTHOR]

This paper introduces a paradigm shift in atomic force microscope (AFM) scan control, leveraging an artificial intelligence (AI)-based controller. In contrast to conventional control methods, which either show a limited performance, such as proportional integral differential (PID) control, or which purely focus on mathematical optimality as classical optimal control approaches, our proposed AI approach redefines the objective of control for achieving practical optimality. This presented AI controller minimizes the root-mean-square control deviations in routine scans by a factor of about 4 compared to PID control in the presented setup and also showcases a distinctive asymmetric response in complex situations, prioritizing the safety of the AFM tip and sample instead of the lowest possible control deviations. The development and testing of the AI control concept are performed on simulated AFM scans, demonstrating its huge potential. Highlights: Artificial intelligence-based asymmetric controller for the highest tip and sample safety with AFM scans. Potential to significantly outperform proportional integral derivative control in classical scan situations. Dynamic change between classically optimal and asymmetric behavior depending on the scan situation. [ABSTRACT FROM AUTHOR]

In this paper, a novel data-centric approach is proposed for solving the 3D mesh segmentation problem. The method uses node2vec, a semi-supervised learning algorithm, to create vector embedding representations for each node in a 3D mesh graph. This makes the mesh data more compact and easier to process which is important for reducing computation costs. K-Means clustering is then used to cluster each node according to their node embedding information. This data-centric approach is more computationally efficient than other complex models such as CNN and RNN. The main contribution of this study is the development of a data-centric AI framework that combines node2vec embedding, machine learning, and deep learning techniques. The use of cosine similarity is also adapted to compare and evaluate the trained node embedding vectors with different hyperparameters. Additionally, a new algorithm is developed to determine the optimal cluster number using geodesic distance on the 3D mesh. Overall, this approach provides competitive results compared to existing mesh segmentation methods. [ABSTRACT FROM AUTHOR]

The manufacturing sector is a fundamental pillar of worldwide economies, contributing markedly to global economic growth. However, the manufacturing industry is persistently confronted with issues impeding its development and expansion, such as manpower shortages, safety concerns, high initial investment for installation, and long return on investment. Within this context, machine tending has become a crucial component of the manufacturing process and potentially serves as a viable solution to the afore-mentioned predicaments. Over the past 5 years, implementing automated machine-tending systems has widely extended from simulation or laboratory environments to practical applications in manufacturing workshops as robotics and artificial intelligence develop rapidly. To fully benefit from the potential of machine-tending applications, it is necessary to comprehend and tackle its associated challenges. Therefore, this paper aims to contribute to the evolution of machine-tending applications by investigating the impacts of emerging trends of advanced technologies, such as autonomous mobile robots, computer vision, machine learning, and deep learning. This systematic literature review is based on the Protocol of Preferred Reporting Items for Systematic Review and Meta-Analyses to analyze the 50 scientific literature related to machine tending in the last five years. [ABSTRACT FROM AUTHOR]

Microelectronics production failure analysis is an important step in improving product quality and development. In fact, the understanding of the failure mechanisms and therefore the implementation of corrective actions on the cause of the failure depend on the results of this analysis. These analyses are saved under textual features format. Then such data need first to be preprocessed and vectorized (converted to numeric). Second, to overcome the curse of dimensionality caused by the vectorisation process, a dimension reduction is applied. A two-stage variable selection and feature extraction is used to reduce the high dimensionality of a feature space. We are first interested in studying the potential of using an unsupervised variable selection technique, the genetic algorithm, to identify the variables that best demonstrate discrimination in the separation and compactness of groups of textual data. The genetic algorithm uses a combination of the K-means or Gaussian Mixture Model clustering and validity indices as a fitness function for optimization. Such a function improves both compactness and class separation. The second work looks into the feasibility of applying a feature extraction technique. The adopted methodology is a Deep learning algorithm based on variational autoencoder (VAE) for latent space disentanglement and Gaussian Mixture Model for clustering of the latent space for cluster identification. The last objective of this paper is to propose a new methodology based on the combination between variational autoencoder (VAE) for the latent space disentanglement, and genetic algorithm (GA) to find, in an unsupervised way, the latent variables allowing the best discrimination of clusters of failure analysis data. This methodology is called VAE-GA. Experiments on textual datasets of failure analysis demonstrate the effectiveness of the VAE-GA proposed method which allows better discrimination of textual classes compared to the use of GA or VAE separately or the combination of PCA with GA (PCA-GA) or a simple Auto-encoders with GA (AE-GA). [ABSTRACT FROM AUTHOR]

Due to the advancement of high definition, 5G technologies, the Internet of Things, and Artificial Intelligence, the demand for optical networks has increased widely. Optical communication networks offer various metrics, including high transmission capacity, efficient anti-interference, minimum transmission loss and robustness, and so on, that offer opportunities for communication networks. To satisfy the optical network demands, effective network resource utilization is essential. Therefore, developing a tool with improved Quality of Transmission (QoT) accuracy in optical networks is necessary. Recently, Artificial intelligence (AI) approaches have provided various opportunities to resolve these issues and the deep learning (DL) algorithms offer improved performance over the conventional methods. This paper developed a novel DL-based cognitive QoT prediction model for Quantum optical communication networks. This proposed model predicts the QoT for the QoS (Quality of Service) setup using the DL model with the transmission computation. The proposed model utilized an optimized DL model called CNN-LSTM for the prediction process using the signal and link characteristics as input features. The DL model is trained using the transmission equations. The hyperparameters of the neural network are optimized using frog leap optimization to improve the predictive performance. The experimental results highlight the enhanced and improved version of the proposed model, and the results are compared with the conventional systems in terms of performance measures. [ABSTRACT FROM AUTHOR]

Given that AI has been developing quickly in recent years, its usage has become increasingly important. It has had a significant influence on a variety of industries, including sports. Although not many experts are discussing it, the use of AI in sports has become commonplace. Predictive analytics has made it possible for many different kinds of athletic events to produce more precise outcomes and judgements. Making the game more difficult on and off the pitch is one of sports AI's main objectives. As a result, it is critical for sports firms to always stay current. As we analyse this document, our goal is to comprehend and research fresh, cutting-edge ways to apply artificial intelligence (AI) to the world of sports. Elite sports require objective evaluation of an athlete's performance to enable in-depth quantum photonics research. The shortcomings of manual performance analysis techniques are solved by the application of automatic detection as well as recognition of sport-specific motions. Inertial measurement unit (IMU) and/or computer vision data inputs were used in this work to recognise sport-specific movements. The goal of the study was to conduct a comprehensive evaluation of literature on ML and DL for these purposes. There was a multi-database search done. Included papers must to have examined a sport-specific movement and used machine learning or deep learning techniques to construct a model. [ABSTRACT FROM AUTHOR]

With an aim to eliminate or reduce the spread of hate content across social media platforms, the development of artificial intelligence supported computational predictors is an active area of research. However, diversity of languages hinders development of generic predictors that can precisely identify hate content. Several language-specific hate speech detection predictors have been developed for most common languages including English, Chinese and German. Specifically, for Urdu language a few predictors have been developed and these predictors lack in predictive performance. The paper in hand presents a precise and explainable deep learning predictor which makes use of advanced language modelling strategies for the extraction of semantic and discriminative patterns. Extracted patterns are utilized to train an attention-based novel classifier that is competent in precisely identifying hate content. Over coarse-grained benchmark dataset, the proposed predictor significantly outperforms state-of-the-art predictor by 8.7% in terms of accuracy, precision and F1-score. Similarly, over fine-grained dataset, in comparison with state-of-the-art predictor, it achieves performance gain of 10.6%, 17.6%, 18.6% and 17.6% in terms of accuracy, precision, recall and F1-score. [ABSTRACT FROM AUTHOR]

In recent years, developments in the fields of computer vision and artificial intelligence have created new opportunities for studying sports performance. With advancements in computer vision and artificial intelligence, it is now possible to use massive volumes of video data to get deeper insights into sports dynamics, especially in precision-based video sports like table tennis. To develop skills, a thorough examination of player movements is required. With the development of vision-based human posture recognition, computers can function like humans and derive intelligent judgments from outside data. This paper presents a novel method that uses graph convolutional neural networks (GCNNs) to detect and identify improper postures and movements in table tennis players. In addition to conventional methods, the proposed method dissects the human skeleton into finely detailed head, trunk, and leg features. Deep-level features that offer a more comprehensive understanding of athlete movements are extracted after feeding these features into the network. The softmax classifier combines these features to produce the final recognition result. The effectiveness of this approach has been evaluated through extensive experimentation. The GCN model performs remarkably well, with accuracy rates of 86.4% on the NTU-RGB + D dataset and 79.1% on the COCO dataset. This accomplishment is significant because the model consistently yields accurate detection results, even in complex and occlusion-filled scenes. In comparison tests, the proposed model performs better than GraphSAGE, GAT, ChebNet, GIN, and GC-LSTM in identifying relevant body part movements in table tennis players while ignoring irrelevant ones. According to proposed experiments, this enhances the recognition of improper postures in most table tennis actions. [ABSTRACT FROM AUTHOR]

As the economy has grown rapidly in recent years, more and more people have begun putting their money into the stock market. Thus, predicting trends in the stock market is regarded as a crucial endeavor, and one that has proven to be more fruitful than others. Profitable investments will result in rising stock prices. Investors face significant difficulties making stock market-related predictions due to the lack of movement and noise in the data. In this paper, a new system for predicting stock market prices is introduced, namely stock market prediction based on deep leaning (SMP-DL). SMP-DL splits into two stages, which are (i) data preprocessing (DP) and (ii) stock price's prediction (SP2). In the first stage, data are preprocessed to obtain cleaned ones through several stages which are detect and reject missing value, feature selection, and data normalization. Then, in the second stage (e.g., SP2), the cleaned data will pass through the used predicted model. In SP2, long short-term memory (LSTM) combined with bidirectional gated recurrent unit (BiGRU) to predict the closing price of stock market. The obtained results showed that the proposed system perform well when compared to other existing methods. As RMSE, MSE, MAE, and R2 values are 0.2883, 0.0831, 0.2099, and 0.9948. Moreover, the proposed method was applied using different datasets and it performs well. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) provides a collaborative infrastructure to communicate smart devices with cloud-edge healthcare applications, medical devices, wearable biosensors, etc. On the other hand, crowd counting as one of computer vision approaches is an emerging topic to detect any objects with static or dynamic mobility in the IoT environments. Smart crowd counting enables pattern recognition for many intelligent applications such as microbiology, surveillance, healthcare systems, crowdedness estimation, and other environmental case studies. According to complicated capturing systems in the IoT environments, crowd counting methods can influence on performance of object detection in the critical case studies using Artificial Intelligence (AI)-based approaches such as machine learning, deep learning, collaborative learning, fuzzy logic and meta-heuristic algorithms. This paper provides a new comprehensive technical analysis for existing AI-based crowd counting approaches in healthcare and medical systems, biotechnology and IoT environments. Meanwhile, it presents a discussion on the existing case studies with respect to analyzing technical aspects and applied algorithms to enhance pattern prediction factors. Finally, some new innovative efforts and challenges are presented for new research upcoming and open issues. [ABSTRACT FROM AUTHOR]

Semantic relatedness is one of the most significant aspects of natural language processing. It has been identified as a critical technology for developing intelligent systems like Siri, Microsoft Ice, Cortana, and Xiaoai. In 2014, SemEval ranked SR as the top task. While many existing studies have focused on analyzing the entailment of single phrases, advancements in deep learning have made it possible to analyze complete sentences or texts. While the natural parallelism of membrane computing has shown promise for data processing, harnessing this potential to advance semantic relatedness remains an open problem yet to be tackled. This paper proposes a novel Siamese Manhattan LSTM-SNP approach (SiMaLSTM-SNP) for the SR problem. The approach uses a collaborative Word2vec and 10-Layer Attention strategy to represent and extract sentence pairs and a Siamese LSTM-SNP structure to calculate the hidden states of sentences. The multi-head self-attention layer identifies text associations and redistributes hidden state weights. The last hidden state is extracted, and the relatedness score is calculated using the Manhattan distance. The experiments demonstrate that SiMaLSTM-SNP outperforms 17 classical SR baselines and 7 novel approaches on the standard datasets SICK and STS in terms of mean square error performance. This indicates that SiMaLSTM-SNP can accurately capture the semantic distinction between two sentences and effectively preserve their semantic information. [ABSTRACT FROM AUTHOR]

Estimating the axial force of rock bolts is vital for the stability and safety of underground excavations. Current methods suffer from being labor-intensive, time-consuming, and prone to inaccuracies. This paper presents a novel method for estimating the axial force of a rock bolt, a critical element in underground engineering, by analyzing the deformation field of the bearing plate. This innovative approach combines 3D laser scanning for data acquisition with a developed convolutional neural network (CNN) model for data processing, resulting in an efficient and non-destructive external monitoring technique. The proposed method was verified with 27 laboratory experiments. The results showed an accurate estimation of the axial forces of rock bolts with an average error of ± 5 kN, underlining the method's potential for in-situ application in underground engineering. This research not only contributes to the development of intelligent systems in rock mechanics and engineering but also has significant implications for industries, such as mining, tunneling, and underground engineering. Highlights: The study introduces a novel method that utilizes 3D laser scanning and a developed CNN model to accurately estimate the axial force of rock bolts. The proposed method achieves accurate estimations of rock bolts' axial forces with an average error of ± 5 kN. Future work includes the integration of object detection algorithms for large-scale 3D laser scanning and the creation of a database of various bearing plates to enhance the method's robustness. [ABSTRACT FROM AUTHOR]

Objectives: To develop a deep learning methodology that distinguishes early from late stages of avascular necrosis of the hip (AVN) to determine treatment decisions. Methods: Three convolutional neural networks (CNNs) VGG-16, Inception ResnetV2, InceptionV3 were trained with transfer learning (ImageNet) and finetuned with a retrospectively collected cohort of (n = 104) MRI examinations of AVN patients, to differentiate between early (ARCO 1–2) and late (ARCO 3–4) stages. A consensus CNN ensemble decision was recorded as the agreement of at least two CNNs. CNN and ensemble performance was benchmarked on an independent cohort of 49 patients from another country and was compared to the performance of two MSK radiologists. CNN performance was expressed with areas under the curve (AUC), the respective 95% confidence intervals (CIs) and precision, and recall and f1-scores. AUCs were compared with DeLong's test. Results: On internal testing, Inception-ResnetV2 achieved the highest individual performance with an AUC of 99.7% (95%CI 99–100%), followed by InceptionV3 and VGG-16 with AUCs of 99.3% (95%CI 98.4–100%) and 97.3% (95%CI 95.5–99.2%) respectively. The CNN ensemble the same AUCs Inception ResnetV2. On external validation, model performance dropped with VGG-16 achieving the highest individual AUC of 78.9% (95%CI 51.6–79.6%) The best external performance was achieved by the model ensemble with an AUC of 85.5% (95%CI 72.2–93.9%). No significant difference was found between the CNN ensemble and expert MSK radiologists (p = 0.22 and 0.092 respectively). Conclusion: An externally validated CNN ensemble accurately distinguishes between the early and late stages of AVN and has comparable performance to expert MSK radiologists. Clinical relevance statement: This paper introduces the use of deep learning for the differentiation between early and late avascular necrosis of the hip, assisting in a complex clinical decision that can determine the choice between conservative and surgical treatment. Key Points: • A convolutional neural network ensemble achieved excellent performance in distinguishing between early and late avascular necrosis. • The performance of the deep learning method was similar to the performance of expert readers. [ABSTRACT FROM AUTHOR]

The challenges posed by the future wireless communication network, which will be a huge system with more complex structures, diverse functions, and massive communication ends, will be addressed by intelligent wireless communication technologies. These technologies are playing an increasingly important role in network architecture, computing architecture, resource allocation algorithm design, etc., thanks to the rapid development of artificial intelligence technologies, particularly the deep learning technologies, and their extensive application in various domains. In this paper, an endogenous intelligent architecture is developed to effectively clarify and understand in-depth the relationship among the factors by constructing wireless knowledge graph for the air interface transmission, the core network, as well as the network environment, and so on. Furthermore, the knowledge graph simultaneously reveals the structure and operation mechanism of the whole wireless communication networks. Cause tracing, intelligent optimization, and performance evaluation are sequentially implemented based on the knowledge graph, thus forming a complete closed-loop for endogenous intelligent wireless communication networks. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bi-directional Encoder Representations from Transformers (ALBERT)- Bi-directional Long Short-Term Memory (BiLSTM)-Conditional Random Fields (CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology. [ABSTRACT FROM AUTHOR]

The dominant intrusion detection models in internet of things industrial internet of things cybersecurity use network-based datasets. The Modbus protocol is one of the most often targeted protocols and cyberattacks against IoT/IIoT devices have grown to be a major threat in recent years. Due to the intricacy of the protocol and the quick evolution of cyber threats, detecting these attacks using conventional techniques might be difficult. This paper proposes an architecture that consistently outperforms the state-of-the-art methods of performing intrusion Detection that includes binary classification of whether an intrusion occurred or not and multi-class classification that classifies the different types of attacks using an embedding layer in a neural network to model the register values. The best accuracy results were obtained with a convolutional neural network, with an accuracy of 98.91% in the Modbus Binary dataset, a fully connected neural network with an accuracy of 98.06% in the multi-class classification of the Modbus dataset, and long short-term memory neural networks with an accuracy of 99.97%, 99.7%, and 80.20% in Binary, multi-class, and multi-class sub-categories, respectively which conclude that the proposed architecture performs consistently better than the control NN. Three NN are designed with and without the proposed architecture. All experiments performed in this paper conclude that the proposed architecture performs consistently better than the control NN. This paper shows that a NN with an embedding function can effectively be used to model whether an attack occurred on a device and the class of attack that occurred. This network can be utilized in the future to lessen DoS attacks and other types of network attacks. The network will be able to protect itself against a lot of damage if attacks can be predicted either before they occur or at the same moment they are launched. [ABSTRACT FROM AUTHOR]

The paper considers real-time cyberattack detection methods based on an algebraic approach. The author has applied the algebraic matching method, which relies on solving behavioral equations and algebraic modeling. The paper proposes to improve the efficiency of attack detection and prevention by combining a neural network that classifies attacks with an algebraic matching method. This scheme is called a cognitive network, the concept of which was first conceived in 2005. The cognitive network properties, such as double and self-learning, are discussed. The technological line for cyberattack detection using cognitive networks is presented. [ABSTRACT FROM AUTHOR]

The usage of intelligent IoT devices is exponentially rising, and so the possibility of attacks in the IoT surfaces. The deep leaning algorithms are competent for directing the sanctuary investigation of IoT systems but have not upgraded the analysis of potential attacks in IoT. This paper aims to advance deep learning methods to create upgraded security strategies for IoT frameworks quickly. The study of the IoT security threats identified with inalienable or recently presented risks is done. Also, this paper does a quick examination of different possible attack surfaces for the IoT framework, and the potential risks identified with each character. The systematic survey of deep learning methods for IoT security and the existence of the chances, focal points, and weaknesses of every strategy opens the door significant for future research. [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

With the advancements in mobile technology, a large amount of data is generated by end devices, which has created a renewed interest in developing new AI-based applications for gaining insights into these data. However, most of these distributed applications need data aggregated at a central server, which has posed severe bandwidth, latency, security, and privacy issues. This paper presents OSGAN (One-Shot distributed learning algorithm using Generative Adversarial Networks), a generic framework that trains a generative adversarial network (GAN) at each client and uses GAN's generative capabilities to create sample data at the server. The server aggregates these data from various clients, builds a deep learning model and sends its parameters back to the clients in one communication round, i.e. the exchange of information between the clients and the server happens only once. In this paper, we present the design and implementation of OSGAN and evaluate its performance by comparing it with the state-of-the-art federated learning (FL) and central training algorithms for both IID and non-IID distribution of data. Our experiments on multiple datasets show that our proposed approach achieves a similar accuracy when compared with both FL and central training algorithms. Specifically, the accuracy drop with OSGAN is maintained within 2% for multiple datasets and multiple numbers of clients. Our results show that the proposed approach reduces the amount of data transfer by almost 98% when compared with federated learning and close to 80% when compared with the central learning approach thereby providing substantial benefit in terms of saving the bandwidth. [ABSTRACT FROM AUTHOR]

The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China's large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development. [ABSTRACT FROM AUTHOR]

With the actual demand of international communication, intelligent translation of English has become a key direction of artificial intelligence development in the current English field. For the existing English intelligent translation, how to deal with the massive data effectively is always a big problem. Therefore, it is necessary to use the principle of machine learning to optimize the English intelligent translation model. The purpose of this paper is to optimize the existing English intelligent translation model through spectral clustering to remove outliers, so as to make it more suitable for the use of massive data. Moreover, this paper uses deep learning methods to improve on the basis of the PoseNet network structure and adds regularization to the convolutional layer, which reduces the problem of gradient disappearance and reduces the computational complexity. In addition, this paper uses adaptive weighting to remove invalid model assumptions. In the conceptual space of the similarity matrix, the interior point is farther from the origin than the outlier point. The algorithm in this paper can detect and eliminate the outliers in a paragraph of English. At the same time, the normal English content will be classified into data categories, and the final translation result will be obtained. Through the experimental test, it can be seen that the model proposed in this paper has good performance and can cope with massive data, so it has certain superiority. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19), which is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-COV-2), surprised the world in December 2019 and has threatened the lives of millions of people. Countries all over the world closed worship places and shops, prevented gatherings, and implemented curfews to stand against the spread of COVID-19. Deep Learning (DL) and Artificial Intelligence (AI) can have a great role in detecting and fighting this disease. Deep learning can be used to detect COVID-19 symptoms and signs from different imaging modalities, such as X-Ray, Computed Tomography (CT), and Ultrasound Images (US). This could help in identifying COVID-19 cases as a first step to curing them. In this paper, we reviewed the research studies conducted from January 2020 to September 2022 about deep learning models that were used in COVID-19 detection. This paper clarified the three most common imaging modalities (X-Ray, CT, and US) in addition to the DL approaches that are used in this detection and compared these approaches. This paper also provided the future directions of this field to fight COVID-19 disease. [ABSTRACT FROM AUTHOR]

Increasing computational power and improving deep learning methods have made computer vision technologies pervasively common in urban environments. Their applications in policing, traffic management, and documenting public spaces are increasingly common (Ridgeway 2018, Coifman et al. 1998, Sun et al. 2020). Despite the often-discussed biases in the algorithms' training and unequally borne benefits (Khosla et al. 2012), almost all applications similarly reduce urban experiences to simplistic, reductive, and mechanistic measures. There is a lack of context, depth, and specificity in these practices that enables semantic knowledge or analysis within urban contexts, especially within the context of using and occupying urban space. This paper will critique existing uses of artificial intelligence and computer vision in urban practices to propose a new framework for understanding people, action, and public space. This paper revisits Geertz's (1973) use of thick descriptions in generating interpretive theories of culture and activity and uses this lens to establish a framework to approach evaluating the varied uses of computer vision technologies that weigh meaning. By discussing cases of implemented examples of urban computer vision—from LinkNYC and Numina's urban measurements to the Detroit Police's use of DataWorks Plus's facial recognition technology—it proposes a framework for evaluating the thickness of the algorithm's conclusions against the computational method's complexity required to produce that outcome. Further, we discuss how the framework's positioning may differ (and conflict) between different users of the technology, from engineer to urban planner and policymaker, to citizen. This paper also discusses how the current use and training of deep learning algorithms and how this process limits semantic learning and proposes three potential methodologies toward gaining a more contextually specific, urban-semantic, description of urban space relevant to urbanists. This paper contributes to the critical conversations regarding the proliferation of artificial intelligence by challenging the current applications of these technologies in the urban environment by highlighting their failures within this context while also proposing an evolution of these algorithms that may ultimately make them sensitive and useful within this spatial and cultural milieu. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence and data acquisition technology are growing, the research on deep learning algorithm has gone deep into various fields. At this stage, the demand supply matching model under the comprehensive passenger transport hub travel system is obtained by analyzing the travel mode data. This paper takes traffic time as the research direction, uses machine learning and complex network theory to conduct in-depth learning algorithm research, respectively, discusses, explores and forecasts the traffic supply and demand mode data, and explores the traffic mode supply and demand model under the comprehensive passenger transport hub travel service system. It is shown in traffic data that using the prediction form of deep learning to predict traffic conditions and travel pressure can enable traffic managers to master traffic dynamics and lead the direction for future traffic development. Finally, from the perspective of MaaS system, the paper uses big data and information processing methods to explore the supply and demand matching model in terms of transportation modes. The research shows that MaaS system can make overall planning in terms of traffic resources, provide reasonable travel modes for traffic travelers and match corresponding travel services. It not only makes overall planning and guarantee for travel services, but also promotes the sustainable development of the transportation supply and demand system. [ABSTRACT FROM AUTHOR]

Purpose: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies. Methods: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights. The consensus threshold was 75%. Based on the median ranks derived from expert panel opinion and their rank-sum based conversion to importance scores, the category and item weights were calculated. Result: In total, 59 panelists from 19 countries participated in selection and ranking of the items and categories. Final METRICS tool included 30 items within 9 categories. According to their weights, the categories were in descending order of importance: study design, imaging data, image processing and feature extraction, metrics and comparison, testing, feature processing, preparation for modeling, segmentation, and open science. A web application and a repository were developed to streamline the calculation of the METRICS score and to collect feedback from the radiomics community. Conclusion: In this work, we developed a scoring tool for assessing the methodological quality of the radiomics research, with a large international panel and a modified Delphi protocol. With its conditional format to cover methodological variations, it provides a well-constructed framework for the key methodological concepts to assess the quality of radiomic research papers. Critical relevance statement: A quality assessment tool, METhodological RadiomICs Score (METRICS), is made available by a large group of international domain experts, with transparent methodology, aiming at evaluating and improving research quality in radiomics and machine learning. Key points: • A methodological scoring tool, METRICS, was developed for assessing the quality of radiomics research, with a large international expert panel and a modified Delphi protocol. • The proposed scoring tool presents expert opinion-based importance weights of categories and items with a transparent methodology for the first time. • METRICS accounts for varying use cases, from handcrafted radiomics to entirely deep learning-based pipelines. • A web application has been developed to help with the calculation of the METRICS score (https://metricsscore.github.io/metrics/METRICS.html) and a repository created to collect feedback from the radiomics community (https://github.com/metricsscore/metrics). [ABSTRACT FROM AUTHOR]

Autoregressive language models, which use deep learning to produce human-like texts, have surged in prevalence. Despite advances in these models, concerns arise about their equity across diverse populations. While AI fairness is discussed widely, metrics to measure equity in dialogue systems are lacking. This paper presents a framework, rooted in deliberative democracy and science communication studies, to evaluate equity in human–AI communication. Using it, we conducted an algorithm auditing study to examine how GPT-3 responded to different populations who vary in sociodemographic backgrounds and viewpoints on crucial science and social issues: climate change and the Black Lives Matter (BLM) movement. We analyzed 20,000 dialogues with 3290 participants differing in gender, race, education, and opinions. We found a substantively worse user experience among the opinion minority groups (e.g., climate deniers, racists) and the education minority groups; however, these groups changed attitudes toward supporting BLM and climate change efforts much more compared to other social groups after the chat. GPT-3 used more negative expressions when responding to the education and opinion minority groups. We discuss the social-technological implications of our findings for a conversational AI system that centralizes diversity, equity, and inclusion. [ABSTRACT FROM AUTHOR]

Flower color is one of the most important traits in horticulture, and is one of the characteristics recorded to describe new varieties. In this paper, we examine four large real-world databases of roses and gerberas containing both images and color descriptions, and use state-of-the-art methods to automatically extract color descriptions from the images. Both Deep Learning and methods based on color histograms lead to success rates of approximately 85%. Deep learning has the advantage that no preprocessing is necessary—the more traditional methods lead to additional insight in the final color classification. [ABSTRACT FROM AUTHOR]

As a main electronic material, X-ray circuits are widely used in various electronic devices, and their quality has an important impact on the overall quality of electronic products. In the process of mass production of circuit boards, due to the large number of layers, tight lines and some harmful external factors, circuit board quality may be problematic. Detecting circuit board defects are important for improving the reliability of electronic products. This paper introduces deep learning and artificial intelligence technology to conduct research on the automatic detection of X-ray circuit board defects. The study used a defect detection system to study X-ray circuit boards as a detection object and obtained the structure, lighting system and composition of the detection system. The working principle of the detection system is explained, and the image is preprocessed. Testing the processing performance of the PCB defect detection system, when the number of pixels is 6526, 7028, 7530 and 8032, the time consumption ratios between the proposed detection system and image processing on a traditional PC are 35.17%, 35.4%, 35% and 35.28%, respectively. The experimental results make a certain contribution to the future artificial intelligence X-ray PCB defect automatic diagnosis algorithm. [ABSTRACT FROM AUTHOR]