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This document is a summary of a special issue on advanced machine learning and deep learning techniques for remote sensing. The issue includes 16 research papers that cover a range of topics, including hyperspectral image classification, moving point target detection, radar echo extrapolation, and remote sensing object detection. Each paper introduces a novel approach or model and provides extensive testing and evaluation to demonstrate its effectiveness. The insights shared in this special issue are expected to contribute to future advancements in artificial intelligence-based remote sensing research. [Extracted from the article]

This document is a summary of a special issue in the journal Applied Sciences titled "Artificial Intelligence Technology in Medical Image Analysis." The special issue explores the applications of artificial intelligence (AI) in medical imaging and its impact on diagnostic and therapeutic processes. The use of AI-powered tools in image interpretation has shown exceptional capabilities in detecting and diagnosing medical conditions from imaging data, particularly in radiology. AI also contributes to improving image quality, automating routine tasks, and streamlining healthcare workflows. However, challenges such as data privacy, ethics, and regulatory frameworks need to be addressed for responsible implementation. The special issue includes several research papers that present advancements in automated medical decision support, age estimation, quality assurance, orthotic insole recommendation, tumor identification, thalamus segmentation, medical image classification, hyperparameter optimization, lung disease classification, and thoracic cavity segmentation. These papers demonstrate the potential of AI in improving accuracy, efficiency, and personalized treatment in medical image analysis. The integration of AI into healthcare requires collaboration between AI researchers, healthcare professionals, and regulatory bodies to ensure responsible and effective deployment. The future of AI in medical image analysis holds promise for improved diagnostic accuracy, early disease detection, and personalized treatment strategies. [Extracted from the article]

The Internet of Things (IoT) is a rapidly growing network that shares information over the Internet via interconnected devices. In addition, this network has led to new security challenges in recent years. One of the biggest challenges is the impact of denial-of-service (DoS) attacks on the IoT. The Information-Centric Network (ICN) infrastructure is a critical component of the IoT. The ICN has gained recognition as a promising networking solution for the IoT by supporting IoT devices to be able to communicate and exchange data with each other over the Internet. Moreover, the ICN provides easy access and straightforward security to IoT content. However, the integration of IoT devices into the ICN introduces new security challenges, particularly in the form of DoS attacks. These attacks aim to disrupt or disable the normal operation of the ICN, potentially leading to severe consequences for IoT applications. Machine learning (ML) is a powerful technology. This paper proposes a new approach for developing a robust and efficient solution for detecting DoS attacks in ICN-IoT networks using ML technology. ML is a subset of artificial intelligence (AI) that focuses on the development of algorithms. While several ML algorithms have been explored in the literature, including neural networks, decision trees (DTs), clustering algorithms, XGBoost, J48, multilayer perceptron (MLP) with backpropagation (BP), deep neural networks (DNNs), MLP-BP, RBF-PSO, RBF-JAYA, and RBF-TLBO, researchers compare these detection approaches using classification metrics such as accuracy. This classification metric indicates that SVM, RF, and KNN demonstrate superior performance compared to other alternatives. The proposed approach was carried out on the NDN architecture because, based on our findings, it is the most used one and has a high percentage of various types of cyberattacks. The proposed approach can be evaluated using an ndnSIM simulation and a synthetic dataset for detecting DoS attacks in ICN-IoT networks using ML algorithms. [ABSTRACT FROM AUTHOR]

This document is a summary of a journal article titled "Artificial Intelligence in Modeling and Simulation." The article discusses the integration of artificial intelligence (AI) into modeling and simulation (M&S) processes. It highlights the various applications of AI in fields such as engineering, physics, social sciences, and biology. The article also provides an overview of 11 selected papers from a special issue on AI and M&S, covering topics such as AI techniques for simulation and optimization, AI in agent-based modeling, AI for data processing and classification models, and artificial neural network (ANN) methods for improved M&S. The papers explore different methodologies and approaches to enhance the efficiency and validity of modeling and simulation using AI. The article concludes by emphasizing the progress and diverse uses of AI in M&S and expressing gratitude to the authors, reviewers, and editorial team involved in the special issue. [Extracted from the article]

This document provides a summary of a special issue on the application of machine learning in geotechnical engineering. The issue includes 19 articles that explore different applications of machine learning in this field, such as determining geotechnical parameters, predicting geotechnical disasters, and optimizing construction processes. The articles cover various topics in geotechnical engineering, including underground and foundation engineering, and discuss the use of machine learning algorithms to predict and estimate various parameters and behaviors. While machine learning shows potential in improving predictions, the papers also acknowledge the limitations of purely data-driven models and the need to incorporate mechanical models and improve data collection methods. Overall, these articles provide valuable insights and serve as a starting point for future research in the field. [Extracted from the article]

This paper addresses issues concerning biometric authentication based on handwritten signatures. Our research aimed to check whether a handwritten signature acquired with a mobile device can effectively verify a user's identity. We present a novel online signature verification method using coordinates of points and pressure values at each point collected with a mobile device. Convolutional neural networks are used for signature verification. In this paper, three neural network models are investigated, i.e., two self-made light SigNet and SigNetExt models and the VGG-16 model commonly used in image processing. The convolutional neural networks aim to determine whether the acquired signature sample matches the class declared by the signer. Thus, the scenario of closed set verification is performed. The effectiveness of our method was tested on signatures acquired with mobile phones. We used the subset of the multimodal database, MobiBits, that was captured using a custom-made application and consists of samples acquired from 53 people of diverse ages. The experimental results on accurate data demonstrate that developed architectures of deep neural networks can be successfully used for online handwritten signature verification. We achieved an equal error rate (EER) of 0.63% for random forgeries and 6.66% for skilled forgeries. [ABSTRACT FROM AUTHOR]

The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects. [ABSTRACT FROM AUTHOR]

The digitization of distribution power systems has revolutionized the way data are collected and analyzed. In this paper, the critical task of harnessing this information to identify irregularities and anomalies in electricity consumption is tackled. The focus is on detecting non-technical losses (NTLs) and energy theft within distribution networks. A comprehensive overview of the methodologies employed to uncover NTLs and energy theft is presented, leveraging measurements of electricity consumption. The most common scenarios and prevalent cases of anomalies and theft among consumers are identified. Additionally, statistical indicators tailored to specific anomalies are proposed. In this research paper, the practical implementation of numerous artificial intelligence (AI) algorithms, including the artificial neural network (ANN), ANFIS, autoencoder neural network, and K-mean clustering, is highlighted. These algorithms play a central role in our research, and our primary objective is to showcase their effectiveness in identifying NTLs. Real-world data sourced directly from distribution networks are utilized. Additionally, we carefully assess how well statistical methods work and compare them to AI techniques by testing them with real data. The artificial neural network (ANN) accurately identifies various consumer types, exhibiting a frequency error of 7.62%. In contrast, the K-means algorithm shows a slightly higher frequency error of 9.26%, while the adaptive neuro-fuzzy inference system (ANFIS) fails to detect the initial anomaly type, resulting in a frequency error of 11.11%. Our research suggests that AI can make finding irregularities in electricity consumption even more effective. This approach, especially when using data from smart meters, can help us discover problems and safeguard distribution networks. [ABSTRACT FROM AUTHOR]

Simple Summary: Artificial intelligence has become essential for aiding in different knowledge domains by improving knowledge extraction from raw data and process automation. In dairy production, artificial intelligence offers promising applications in detecting and managing bovine mastitis, the most critical disease affecting the mammary gland in dairy cows, impacting milk production and profitability in dairy farms. This research evaluated the evolution of artificial intelligence applications in bovine mastitis between 2011 and 2021 using the Scopus database and the frequency of terms cited in titles, abstracts, and keywords. We selected the 62 papers that were the most relevant according to their citation index. Our results pointed out that the terms "machine learning" and "mastitis" were the most cited, with a significant increase between 2018 and 2021. There was an increase in artificial intelligence applications for bovine mastitis per country, showing applications primarily aimed at improving the current mastitis detection systems. The most cited model was artificial neural networks. We concluded that using artificial intelligence in bovine mastitis was related to mastitis detection as a vital tool to prevent this disease, considering its major impacts on dairy production and economic return. Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area. [ABSTRACT FROM AUTHOR]

Today, the field of biomedical engineering spans numerous areas of scientific research that grapple with the challenges of intelligent analysis of small datasets. Analyzing such datasets with existing artificial intelligence tools is a complex task, often complicated by issues like overfitting and other challenges inherent to machine learning methods and artificial neural networks. These challenges impose significant constraints on the practical application of these tools to the problem at hand. While data augmentation can offer some mitigation, existing methods often introduce their own set of limitations, reducing their overall effectiveness in solving the problem. In this paper, the authors present an improved neural network-based technology for predicting outcomes when analyzing small and extremely small datasets. This approach builds on the input doubling method, leveraging response surface linearization principles to improve performance. Detailed flowcharts of the improved technology's operations are provided, alongside descriptions of new preparation and application algorithms for the proposed solution. The modeling, conducted using two biomedical datasets with optimal parameters selected via differential evolution, demonstrated high prediction accuracy. A comparison with several existing methods revealed a significant reduction in various errors, underscoring the advantages of the improved neural network technology, which does not require training, for the analysis of extremely small biomedical datasets. [ABSTRACT FROM AUTHOR]

Visual object tracking is a key technology that is used in unmanned aerial vehicles (UAVs) to achieve autonomous navigation. In recent years, with the rapid development of deep learning, tracking algorithms based on Siamese neural networks have received widespread attention. However, because of complex and diverse tracking scenarios, as well as limited computational resources, most existing tracking algorithms struggle to ensure real-time stable operation while improving tracking performance. Therefore, studying efficient and fast-tracking frameworks, and enhancing the ability of algorithms to respond to complex scenarios has become crucial. Therefore, this paper proposes a cross-parallel attention and efficient match transformer for aerial tracking (SiamEMT). Firstly, we carefully designed the cross-parallel attention mechanism to encode global feature information and to achieve cross-dimensional interaction and feature correlation aggregation via parallel branches, highlighting feature saliency and reducing global redundancy information, as well as improving the tracking algorithm's ability to distinguish between targets and backgrounds. Meanwhile, we implemented an efficient match transformer to achieve feature matching. This network utilizes parallel, lightweight, multi-head attention mechanisms to pass template information to the search region features, better matching the global similarity between the template and search regions, and improving the algorithm's ability to perceive target location and feature information. Experiments on multiple drone public benchmark tests verified the accuracy and robustness of the proposed tracker in drone tracking scenarios. In addition, on the embedded artificial intelligence (AI) platform AGX Xavier, our algorithm achieved real-time tracking speed, indicating that our algorithm can be effectively applied to UAV tracking scenarios. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Applied Sciences that focuses on eHealth innovative approaches and applications. It covers various topics including COVID-19 classification, AI in medical imaging, cyber security in healthcare, and the use of Large Language Models (LLMs) for coding diagnoses. The papers provide insights into the latest developments in eHealth and address issues such as data privacy, cyber security, and the challenges of implementing new technologies in healthcare. The document acknowledges the contributions of the authors and reviewers and expresses gratitude for their support. [Extracted from the article]

Using optimal assembly relationships, companies can enhance product quality without significantly increasing production costs. However, predicting Assembly Geometric Errors presents a challenging real-world problem in the manufacturing domain. To address this challenge, this paper introduces a highly efficient Transformer-based neural network model known as Predicting Assembly Geometric Errors based on Transformer (PAGEformer). This model accurately captures long-range assembly relationships and predicts final assembly errors. The proposed model incorporates two unique features: firstly, an enhanced self-attention mechanism to more effectively handle long-range dependencies, and secondly, the generation of positional information regarding gaps and fillings to better capture assembly relationships. This paper collected actual assembly data for folding rudder blades for unmanned aerial vehicles and established a Mechanical Assembly Relationship Dataset (MARD) for a comparative study. To further illustrate PAGEformer performance, we conducted extensive testing on a large-scale dataset and performed ablation experiments. The experimental results demonstrated a 15.3% improvement in PAGEformer accuracy compared to ARIMA on the MARD. On the ETH, Weather, and ECL open datasets, PAGEformer accuracy increased by 15.17%, 17.17%, and 9.5%, respectively, compared to the mainstream neural network models. [ABSTRACT FROM AUTHOR]

This document provides a summary of a special issue on the application of artificial intelligence (AI) in hydraulic engineering. The papers in this issue cover a range of topics including safety monitoring, optimization algorithms, nondestructive sensors, numerical simulations, and experiments. The research presented in these papers is significant for analyzing and managing the safety of major structures in reservoir dams. The authors hope that these papers will be of interest to researchers, designers, and practitioners in the field and will inspire further research. [Extracted from the article]

This paper introduces a numerical methodology for classifying and identifying types of bio-based materials through experimental thermal characterization. In contrast to prevailing approaches that primarily focus on thermal conductivity, our characterization methodology encompasses several thermal parameters. In this paper, the physical characteristics of seven types of bio-based concrete were analyzed, focusing on the thermal properties of palm- and esparto-fiber-reinforced concrete. The proposed method uses artificial intelligence techniques, specifically the k-means clustering approach, to segregate data into homogeneous groups with shared thermal characteristics. This enables the elucidation of insights and recommendations regarding the utilization of bio-based insulation in building applications. The results show that the k-means algorithm is able to efficiently classify the reference concrete (RC) with a performance of up to 71%. Additionally, the technique is more accurate when retaining only six centroids, which, among other things, allows all the characteristics associated with each type of concrete to be grouped and identified. Indeed, whether for k clusters k = 7 or k = 5, the technique was not able to predict the typical characteristics of 2% or 3% esparto concrete (EC). [ABSTRACT FROM AUTHOR]

This review explores recent advancements in hydrogen gas (H2) safety through the lens of artificial intelligence (AI) techniques. As hydrogen gains prominence as a clean energy source, ensuring its safe handling becomes paramount. The paper critically evaluates the implementation of AI methodologies, including artificial neural networks (ANN), machine learning algorithms, computer vision (CV), and data fusion techniques, in enhancing hydrogen safety measures. By examining the integration of wireless sensor networks and AI for real-time monitoring and leveraging CV for interpreting visual indicators related to hydrogen leakage issues, this review highlights the transformative potential of AI in revolutionizing safety frameworks. Moreover, it addresses key challenges such as the scarcity of standardized datasets, the optimization of AI models for diverse environmental conditions, etc., while also identifying opportunities for further research and development. This review foresees faster response times, reduced false alarms, and overall improved safety for hydrogen-related applications. This paper serves as a valuable resource for researchers, engineers, and practitioners seeking to leverage state-of-the-art AI technologies for enhanced hydrogen safety systems. [ABSTRACT FROM AUTHOR]

Deep learning, a crucial technique for achieving artificial intelligence (AI), has been successfully applied in many fields. The gradual application of the latest architectures of deep learning in the field of time series forecasting (TSF), such as Transformers, has shown excellent performance and results compared to traditional statistical methods. These applications are widely present in academia and in our daily lives, covering many areas including forecasting electricity consumption in power systems, meteorological rainfall, traffic flow, quantitative trading, risk control in finance, sales operations and price predictions for commercial companies, and pandemic prediction in the medical field. Deep learning-based TSF tasks stand out as one of the most valuable AI scenarios for research, playing an important role in explaining complex real-world phenomena. However, deep learning models still face challenges: they need to deal with the challenge of large-scale data in the information age, achieve longer forecasting ranges, reduce excessively high computational complexity, etc. Therefore, novel methods and more effective solutions are essential. In this paper, we review the latest developments in deep learning for TSF. We begin by introducing the recent development trends in the field of TSF and then propose a new taxonomy from the perspective of deep neural network models, comprehensively covering articles published over the past five years. We also organize commonly used experimental evaluation metrics and datasets. Finally, we point out current issues with the existing solutions and suggest promising future directions in the field of deep learning combined with TSF. This paper is the most comprehensive review related to TSF in recent years and will provide a detailed index for researchers in this field and those who are just starting out. [ABSTRACT FROM AUTHOR]

Reservoir computing (RC) systems can efficiently forecast chaotic time series using the nonlinear dynamical properties of an artificial neural network of random connections. The versatility of RC systems has motivated further research on both hardware counterparts of traditional RC algorithms and more-efficient RC-like schemes. Inspired by the nonlinear processes in a living biological brain and using solitary waves excited on the surface of a flowing liquid film, in this paper, we experimentally validated a physical RC system that substitutes the effect of randomness that underpins the operation of the traditional RC algorithm for a nonlinear transformation of input data. Carrying out all operations using a microcontroller with minimal computational power, we demonstrate that the so-designed RC system serves as a technically simple hardware counterpart to the 'next-generation' improvement of the traditional RC algorithm. [ABSTRACT FROM AUTHOR]

The advancement of cutting-edge technologies significantly transforms urban lifestyles and is indispensable in sustainable urban design and planning. This systematic review focuses on the critical role of innovative technologies and digitalization, particularly artificial intelligence (AI), in urban planning through geo-design, aiming to enhance urban life. It begins with exploring the importance of AI and digital tools in revolutionizing contemporary urban planning practices. Through the methodology based on the Systematic Reviews and Meta-Analyses (PRISMA) protocol, this review sifts through relevant literature over the past two decades by categorizing artificial intelligence technologies based on their functionalities. These technologies are examined for their utility in urban planning, environmental modeling, and infrastructure development, highlighting how they contribute to creating smarter and more livable cities. For instance, machine learning techniques like supervised learning excel in forecasting urban trends, whereas artificial neural networks and deep learning are superior in pattern recognition and vital for environmental modeling. This analysis, which refers to the comprehensive evaluation conducted in this Systematic Review, encompasses studies based on diverse data inputs and domains of application, revealing a trend toward leveraging AI for predictive analytics, decision-making improvements, and the automation of complex geospatial tasks in urban areas. The paper also addresses the challenges encountered, including data privacy, ethical issues, and the demand for cross-disciplinary knowledge. The concluding remarks emphasize the transformative potential of innovative technologies and digitalization in urban planning, advocating for their role in fostering better urban life. It also identifies future research avenues and development opportunities. In light of our review findings, this study concludes that AI technologies indeed hold transformative promise for the field of geo-design and urban planning. They have proven instrumental in advancing predictive analytics, refining decision-making, and streamlining complex geospatial tasks. The AI's capacity to process expansive datasets and improve urban planning accuracy has facilitated more sustainable urban development and enhanced the resilience of urban environments. [ABSTRACT FROM AUTHOR]

Predicting bankruptcy within selected industries is crucial because of the potential ripple effects and unique characteristics of those industries. It serves as a risk management tool, guiding various stakeholders in making decisions. While artificial intelligence (AI) has shown high success rates in classification tasks, it remains uncertain whether its use significantly enhances the potential for early warning of impending problems. The following question arises: will classical methods eventually replace the effectiveness of these advanced techniques? This paper sheds light on the fact that even classical methods continue to achieve results that are not far behind, highlighting their enduring importance in financial analysis. This paper aims to develop bankruptcy prediction models for the chemical industry in Slovakia and to compare their effectiveness. Predictions are generated using the classical logistic regression (LR) method as well as AI techniques, artificial neural networks (ANNs), support vector machines (SVMs), and decision trees (DTs). The analysis aims to determine which of the employed methods is the most efficient. The research sample consists of circa 600 enterprises operating in the Slovak chemical industry. The selection of eleven financial indicators used for bankruptcy prediction was grounded in prior research and existing literature. The results show that all of the explored methods yielded highly similar outcomes. Therefore, determining the clear superiority of any single method is a difficult task. This might be partially due to the potentially reduced quality of the input data. In addition to classical statistical methods employed in econometrics, there is an ongoing development of AI-based models and their hybrid forms. The following question arises: to what extent can these newer approaches enhance accuracy and effectiveness? [ABSTRACT FROM AUTHOR]

This document discusses the advancements in AI-based information technologies and their role in improving the quality and security of software and computer systems. It highlights the challenges faced in information processing and the need for specialized approaches in different industries. The document also presents several research contributions in areas such as malware detection, privacy-preserving anomaly detection, mobile application security evaluation, research paper recommendation systems, image encryption, and adaptive cyber defense. These contributions offer innovative solutions and demonstrate their effectiveness in addressing specific issues in the field of AI-based information technologies. [Extracted from the article]

Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI. [ABSTRACT FROM AUTHOR]

This paper presents a computer vision-based approach to chronic subdural hematoma segmentation that can be performed by one click. Chronic subdural hematoma is estimated to occur in 0.002–0.02% of the general population each year and the risk increases with age, with a high frequency of about 0.05–0.06% in people aged 70 years and above. In our research, we developed our own dataset, which includes 53 series of CT scans collected from 21 patients with one or two hematomas. Based on the dataset, we trained two neural network models based on U-Net architecture to automate the manual segmentation process. One of the models performed segmentation based only on the current frame, while the other additionally processed multiple adjacent images to provide context, a technique that is more similar to the behavior of a doctor. We used a 10-fold cross-validation technique to better estimate the developed models' efficiency. We used the Dice metric for segmentation accuracy estimation, which was 0.77. Also, for testing our approach, we used scans from five additional patients who did not form part of the dataset, and created a scenario in which three medical experts carried out a hematoma segmentation before we carried out segmentation using our best model. We developed the OsiriX DICOM Viewer plugin to implement our solution into the segmentation process. We compared the segmentation time, which was more than seven times faster using the one-click approach, and the experts agreed that the segmentation quality was acceptable for clinical usage. [ABSTRACT FROM AUTHOR]

With the continuous development of computer technology, artificial intelligence has been widely applied across various industries. To address the issues of high computational cost and inefficiency in traditional numerical methods, this paper proposes a data-driven artificial intelligence approach for solving high-dimensional neutron transport equations. Based on the AFA-3G assembly model, a neutron transport equation solving model is established using deep neural networks, considering factors that influence the neutron transport process in real engineering scenarios, such as varying temperature, power, and boron concentration. Comparing the model's predicted values with reference values, the average error in the infinite multiplication factor kinf of the assembly is found to be 145.71 pcm (10−5), with a maximum error of 267.10 pcm. The maximum relative error is less than 3.5%, all within the engineering error standards of 500 pcm and 5%. This preliminary validation demonstrates the feasibility of using data-driven artificial intelligence methods to solve high-dimensional neutron transport equations, offering a new option for engineering design and practical engineering computations. [ABSTRACT FROM AUTHOR]

The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks will be easier to implement in small-scale solutions, like those for the Internet of Things, in the future. This paper presents test results where a real system was monitored, and anomalies were detected and predicted. [ABSTRACT FROM AUTHOR]

Over the last decade, the world has witnessed many breakthroughs in artificial intelligence, largely due to advances in deep learning technology. Notably, computer vision solutions have significantly contributed to these achievements. Human face analysis, a core area of computer vision, has gained considerable attention due to its wide applicability in fields such as law enforcement, social media, and marketing. However, existing methods for facial age estimation often struggle with accuracy due to limited feature extraction capabilities and inefficiencies in learning hierarchical representations. This paper introduces a novel framework to address these issues by proposing a Multi-Stage Deep Neural Network (MSDNN) architecture. The MSDNN architecture divides each CNN backbone into multiple stages, enabling more comprehensive feature extraction, thereby improving the accuracy of age predictions from facial images. Our framework demonstrates a significant performance improvement over traditional solutions, with its effectiveness validated through comparisons with the EfficientNet and MobileNetV3 architectures. The proposed MSDNN architecture achieves a notable decrease in Mean Absolute Error (MAE) across three widely used public datasets (MORPH2, CACD, and AFAD) while maintaining a virtually identical parameter count compared to the initial backbone architectures. These results underscore the effectiveness and feasibility of our methodology in advancing the field of age estimation, showcasing it as a robust solution for enhancing the accuracy of age prediction algorithms. [ABSTRACT FROM AUTHOR]

A long-standing research goal is to develop computing technologies that mimic the brain's capabilities by implementing computation in electronic systems directly inspired by its structure, function, and operational mechanisms, using low-power, spike-based neural networks. The Loihi neuromorphic processor provides a low-power, large-scale network of programmable silicon neurons for brain-inspired artificial intelligence applications. This paper exploits the Loihi processors and a theory-guided methodology to enable unsupervised learning of spike patterns. Our method ensures efficient and rapid selection of the network's hyperparameters, enabling the neuromorphic processor to generate attractor states through real-time unsupervised learning. Precisely, we follow a fast design process in which we fine-tune network parameters using mean-field theory. Moreover, we measure the network's learning ability regarding its error correction and pattern completion aptitude. Finally, we observe the dynamic energy consumption of the neuron cores for each millisecond of simulation equal to 23 μ J/time step during the learning and recall phase for four attractors composed of 512 excitatory neurons and 256 shared inhibitory neurons. This study showcases how large-scale, low-power digital neuromorphic processors can be quickly programmed to enable the autonomous generation of attractor states. These attractors are fundamental computational primitives that theoretical analysis and experimental evidence indicate as versatile and reusable components suitable for a wide range of cognitive tasks. [ABSTRACT FROM AUTHOR]

The paper proposes an innovative model able to predict the output signals of resistance and capacitance (RC) low-pass filters for machine-controlled systems. Specifically, the work is focused on the analysis of the parametric responses in the time- and frequency-domain of the filter output signals, by considering a white generic noise superimposed onto an input sinusoidal signal. The goal is to predict the filter output using a black-box model to support the denoising process by means of a double-stage RC filter. Artificial neural networks (ANNs) and random forest (RF) algorithms are compared to predict the output of noisy signals. The work is concluded by defining guidelines to correct the voltage output by knowing the predictions and by adding further RC elements correcting the distorted signals. The model is suitable for the implementation of Industry 5.0 Digital Twin (DT) networks applied to manufacturing processes. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most common causes of death in women. Early signs of breast cancer can be an abnormality depicted on breast images like breast ultrasonography. Unfortunately, ultrasound images contain a lot of noise, which greatly increases the difficulty for doctors to interpret them. In recent years, computer-aided diagnosis (CAD) has been widely used in medical images, reducing the workload of doctors and the probability of misdiagnosis. However, it still faces the following challenges in clinical practice: one is the lack of interpretability, and another is that the accuracy is not high enough. In this paper, we propose a classification model of breast ultrasound images that leverages tumor boundaries as prior knowledge and strengthens the model to guide classification. Furthermore, we employ the advantages of convolutional neural network (CNN) to extract local features and Transformer to extract global features to achieve information balance and complementarity between the two neural network models which increase the recognition performance of the model. Additionally, an explanation method is used to generate visual results, thereby improving the poor interpretability of deep learning models. Finally, we evaluate the model on the BUSI dataset and compare it with other CNN and Transformer models. Experimental results show that the proposed model obtains an accuracy of 0.9870 and an F1 score of 0.9872, achieving state-of-the-art performance. [ABSTRACT FROM AUTHOR]

In this research paper, the behavior of shallow footing with square and rectangular shapes over geosynthetic reinforced soil was studied. A novel geogrid called "3D tube-geogrid" was utilized for this work. The impact of various reinforcement parameters, including the depth of the final layer (z), length (l), inclination (α), filler material used inside the geogrid tube, relative soil density, and the tensile stiffness of the geogrid (EA), were analyzed by running numerical simulations using PLAXIS 3D V20 software. The simulated data were used to quantify the relationship between the ultimate bearing capacity of the soil and the reinforcement parameters. Several artificial intelligence (AI) techniques, such as linear regression analysis, a random forest model, and an artificial neural network (ANN), were employed on the generated dataset. To evaluate the preciseness of these techniques, various statistical indicators, such as the squared correlation coefficient (R2), mean absolute percentage error (MAPE), mean squared error (MSE), and root-mean-square error (RMSE), were calculated, and error percentages of 20.98%, 12.5%, and 6.4% were obtained for the linear regression, random forest, and ANN, respectively. The numerical study determined the optimal values of the reinforcement parameters length, z/B, inclination, and filling material to be 4B, 3, 0°, and aggregate, respectively. [ABSTRACT FROM AUTHOR]

This paper aims to study and demonstrate the possibilities of using reinforcement learning for the synthesis of multi-objective controllers for radial actively lubricated hybrid fluid film bearings (ALHBs), which are considered to be complex multi-physical systems. In addition to the rotor displacement control problem being typically solved for active bearings, the proposed approach also includes power losses due to friction and lubricant pumping in ALHBs among the control objectives to be minimized by optimizing the lubrication modes. The multi-objective controller was synthesized using the deep Q-network (DQN) learning technique. An optimal control policy was determined by the DQN agent during its repetitive interaction with the simulation model of the rotor system with ALHBs. The calculations were sped up by replacing the numerical model of an ALHB with its surrogate ANN-based counterpart and by predicting the shaft displacements in response to operation of two independent control loops. The controller synthesized considering the formulated reward function for DQN agent is able to find a stable shaft position that reduces power losses by almost half compared to the losses observed when using a passive system. It also is able to prevent the established limit of the minimum fluid film thickness being exceeded to avoid possible system damage, for example, when the rotor is unbalanced during the operation. Analysis of the development process and the results obtained allowed us to draw conclusions about the main advantages and disadvantages of the considered approach, and also allowed us to identify some important directions for further research. [ABSTRACT FROM AUTHOR]

In this paper, a technology named SCM−ANN combining physical scattering mechanisms and artificial intelligence is proposed to realize radar cross-section (RCS) extrapolation of non-cooperative conductor targets with higher efficiency. Firstly, an adaptive scattering center (SC) extraction algorithm is used to construct the scattering center model (SCM) for non-cooperative targets from radar echoes in the low-frequency band (LFB). Secondly, an artificial neural network (ANN) is constructed to capture the nonlinear relationship between the real LFB echoes and those reconstructed from the SCM. Finally, the SCM is used to reconstruct echoes in the high-frequency band (HFB), and these reconstructions, together with the trained ANN, optimize the extrapolated HFB RCS. For the SCM−ANN technology, physical mechanistic modes are used for trend prediction, and artificial intelligence is used for regression optimization based on trend prediction. Simulation results show that the proposed method can achieve a 50% frequency extrapolation range, with an average prediction error reduction of up to 40% compared with the traditional scheme. By incorporating physical mechanisms, this proposed approach offers improved accuracy and an extended extrapolation range compared with the RCS extrapolation techniques relying solely on numerical prediction. [ABSTRACT FROM AUTHOR]

Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model's behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners. [ABSTRACT FROM AUTHOR]

Intelligent control methodologies and artificial intelligence (AI) are essential components for the efficient management of energy storage modern systems, specifically those utilizing superconducting magnetic energy storage (SMES). Through the implementation of AI algorithms, SMES units are able to optimize their operations in real time, thereby maximizing energy efficiency. To have a more advanced understanding of this issue, DynamoMan is presented in this paper. For use with SMES systems, DynamoMan, an Artificial Neural Network (ANN)-tuned Fractional Order PID Brain Emotional Learning-Based Intelligent Controller (ANN-FOPID-BELBIC), has been developed. ANN tuning is employed to optimize the key settings of the reward/penalty generator of a BELBIC, which are important for its overall efficacy. Following this, DynamoMan is integrated into the SMES control system and compared to scenarios in which a BELBIC, PID, PI, and P are utilized. The findings indicate that DynamoMan performs considerably better than other models, demonstrating robust and control attributes alongside a considerably reduced period of settling time, especially when incorporated with the power grid. [ABSTRACT FROM AUTHOR]

Stuck pipe refers to the accidental phenomenon whereby drilling tools are stuck in a well during the drilling process and cannot move freely due to various reasons. As a result, the stuck pipe can consume a lot of manpower and material resources. With the development of artificial intelligence, the intelligent prediction and identification of stuck pipe risk has gradually advanced. However, there are usually only a few stuck samples, so the intelligent model is not sufficient to excavate the stuck feature law, and then the model overfitting phenomenon occurs. Regarding the above issue, this paper proposed a limited incident dataset method based on data augmentation. Firstly, in terms of data processing, by applying percentage scaling and random dithering to the original data and combining it with GAN to generate new data, the training dataset was effectively extended, solving the problem of insufficient sample size. Then, in the selection and training of the intelligent model, an LSTM neural network model with an attention mechanism (ATT-LSTM) is introduced. By applying the attention mechanism in each time step, the model can dynamically adjust the degree of attention to different parts of the sequence and better capture the key information in the data, which improve the accuracy of the recognition and the generalization ability of the model. By testing the trained model on field data, the test results show that the method achieves more significant performance improvement on the stuck pipe recognition task, and the prediction accuracy of the intelligent model increases by 21.31% after data enhancement. [ABSTRACT FROM AUTHOR]

In this study, the prediction of n-value (index-value) surfaces—a key indicator of the field and temperature dependence of critical current density in superconductors—across various high-temperature superconducting materials is addressed using a deep learning modeling approach. As superconductors play a crucial role in advanced technological applications in aerospace and fusion energy sectors, improving their performance model is essential for both practical and academic research purposes. The feed-forward deep learning network technique is employed for the predictive modeling of n-value surfaces, utilizing a comprehensive dataset that includes experimental data on material properties and operational conditions affecting superconductors' behavior. The model demonstrates enhanced accuracy in predicting n-value surfaces when compared to traditional regression methods by a 99.62% goodness of fit to the experimental data for unseen data points. In this paper, we have demonstrated both the interpolation and extrapolation capabilities of our proposed DFFNN technique. This research advances intelligent modeling in the field of superconductivity and provides a foundation for further exploration into deep learning predictive models for different superconducting devices. [ABSTRACT FROM AUTHOR]

The article discusses the growth and prospects of artificial intelligence (AI) technology, particularly following the launch of ChatGPT in 2023. The global AI market is expected to reach over USD 2.5 trillion by 2032, and ChatGPT has already gained 200 million users within a year. The article emphasizes the need for research papers on deep structure AI and interdisciplinary applications of large-scale language models (LLMs). It also highlights the importance of hardware-based deep learning AI and the transparency and interpretability of deep neural networks. The article concludes by mentioning promising areas in the AI section, such as electronic interfaces, self-driving cars, emotional systems, and healthcare systems. [Extracted from the article]

Simple Summary: Breast cancer is a significant cause of female cancer-related deaths in the US. Checking how severe the cancer is helps in planning treatment. Modern AI methods are good at grading cancer, but they are not used much in hospitals yet. We developed and utilized ensemble deep learning algorithms for addressing the tasks of classifying (1) breast cancer subtype and (2) breast cancer invasiveness from whole slide image (WSI) histopathology slides. The ensemble models used were based on convolutional neural networks (CNNs) known for extracting distinctive features crucial for accurate classification. In this paper, we provide a comprehensive analysis of these models and the used methodology for breast cancer diagnosis tasks. Cancer diagnosis and classification are pivotal for effective patient management and treatment planning. In this study, a comprehensive approach is presented utilizing ensemble deep learning techniques to analyze breast cancer histopathology images. Our datasets were based on two widely employed datasets from different centers for two different tasks: BACH and BreakHis. Within the BACH dataset, a proposed ensemble strategy was employed, incorporating VGG16 and ResNet50 architectures to achieve precise classification of breast cancer histopathology images. Introducing a novel image patching technique to preprocess a high-resolution image facilitated a focused analysis of localized regions of interest. The annotated BACH dataset encompassed 400 WSIs across four distinct classes: Normal, Benign, In Situ Carcinoma, and Invasive Carcinoma. In addition, the proposed ensemble was used on the BreakHis dataset, utilizing VGG16, ResNet34, and ResNet50 models to classify microscopic images into eight distinct categories (four benign and four malignant). For both datasets, a five-fold cross-validation approach was employed for rigorous training and testing. Preliminary experimental results indicated a patch classification accuracy of 95.31% (for the BACH dataset) and WSI image classification accuracy of 98.43% (BreakHis). This research significantly contributes to ongoing endeavors in harnessing artificial intelligence to advance breast cancer diagnosis, potentially fostering improved patient outcomes and alleviating healthcare burdens. [ABSTRACT FROM AUTHOR]

The latest survey results show an increase in accidents on the roads involving pedestrians and cyclists. The reasons for such situations are many, the fault actually lies on both sides. Equipping vehicles, especially autonomous vehicles, with frequency-modulated continuous-wave (FMCW) radar and dedicated algorithms for analyzing signals in the time–frequency domain as well as algorithms for recognizing objects in radar imaging through deep neural networks can positively affect safety. This paper presents a method for recognizing and distinguishing a group of objects based on radar signatures of objects and a special convolutional neural network structure. The proposed approach is based on a database of radar signatures generated on pedestrian, cyclist, and car models in a Matlab environment. The obtained results of simulations and positive tests provide a basis for the application of the system in many sectors and areas of the economy. Innovative aspects of the work include the method of discriminating between multiple objects on a single radar signature, the dedicated architecture of the convolutional neural network, and the use of a method of generating a custom input database. [ABSTRACT FROM AUTHOR]

The increasing growth in knowledge about the functioning of the nervous system of mammals and humans, as well as the significant neuromorphic technology developments in recent decades, has led to the emergence of a large number of brain–computer interfaces and neuroprosthetics for regenerative medicine tasks. Neurotechnologies have traditionally been developed for therapeutic purposes to help or replace motor, sensory or cognitive abilities damaged by injury or disease. They also have significant potential for memory enhancement. However, there are still no fully developed neurotechnologies and neural interfaces capable of restoring or expanding cognitive functions, in particular memory, in mammals or humans. In this regard, the search for new technologies in the field of the restoration of cognitive functions is an urgent task of modern neurophysiology, neurotechnology and artificial intelligence. The hippocampus is an important brain structure connected to memory and information processing in the brain. The aim of this paper is to propose an approach based on deep neural networks for the prediction of hippocampal signals in the CA1 region based on received biological input in the CA3 region. We compare the results of prediction for two widely used deep architectures: reservoir computing (RC) and long short-term memory (LSTM) networks. The proposed study can be viewed as a first step in the complex task of the development of a neurohybrid chip, which allows one to restore memory functions in the damaged rodent hippocampus. [ABSTRACT FROM AUTHOR]

Obtaining mathematical models of nonlinear cyber–physical systems for use in controller design is both difficult and time consuming. In this paper, an ANN-based method is proposed to design a controller for a nonlinear system that does not require a mathematical model. The developed ANN-based control algorithm is implemented directly on a real-time field controller, and its performance is evaluated without the use of auxiliary devices, such as PCs or workstations. By executing machine learning algorithms on local devices or embedded systems, edge artificial intelligence (Edge AI) with transfer learning gives priority to processing data at the source, minimizing the necessity for continuous connectivity to remote servers. The control algorithm was developed using the Matlab Simulink environment. The first and second ANNs were cascaded, wherein the first ANN computes the appropriate pressure signal for the given displacement, while the second predicts the force based on the pressure value from the first ANN. Subsequently, the ANN-based control algorithm was converted to SCL code using the Simulink PLC Coder and deployed on the PLC for operation. The algorithm was tested using two different scenarios. The conducted tests demonstrated the successful prediction of pressure signals corresponding to the targeted displacement values and accurate estimation of force values. Experimental work was carried out on PAM manipulators as a nonlinear model application, and the obtained results were discussed. [ABSTRACT FROM AUTHOR]

Simple Summary: Risk assessment in early breast cancer is critical for clinical decisions, but defining risk categories poses a significant challenge. The integration of conventional histopathology and biomarkers with artificial intelligence (AI) techniques, including machine learning and deep learning, has the potential to offer more precise information. AI applications extend beyond detection to histological subtyping, grading, and molecular feature identification. The successful integration of AI into clinical practice requires collaboration between histopathologists, molecular pathologists, computational pathologists, and oncologists to optimize patient outcomes. Effective risk assessment in early breast cancer is essential for informed clinical decision-making, yet consensus on defining risk categories remains challenging. This paper explores evolving approaches in risk stratification, encompassing histopathological, immunohistochemical, and molecular biomarkers alongside cutting-edge artificial intelligence (AI) techniques. Leveraging machine learning, deep learning, and convolutional neural networks, AI is reshaping predictive algorithms for recurrence risk, thereby revolutionizing diagnostic accuracy and treatment planning. Beyond detection, AI applications extend to histological subtyping, grading, lymph node assessment, and molecular feature identification, fostering personalized therapy decisions. With rising cancer rates, it is crucial to implement AI to accelerate breakthroughs in clinical practice, benefiting both patients and healthcare providers. However, it is important to recognize that while AI offers powerful automation and analysis tools, it lacks the nuanced understanding, clinical context, and ethical considerations inherent to human pathologists in patient care. Hence, the successful integration of AI into clinical practice demands collaborative efforts between medical experts and computational pathologists to optimize patient outcomes. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of deep neural network (DNN)-based signal detection in multiple input, multiple output (MIMO), communication systems. MIMO technology plays a critical role in achieving high data rates and improved capacity in modern wireless communication standards like 5G. However, signal detection in MIMO systems presents significant challenges due to channel complexities. This study conducts a comparative analysis of signal detection techniques within both the single input, single output (SISO), and MIMO frameworks. The analysis focuses on the entire transmission chain, encompassing transmitters, channels, and receivers. The effectiveness of three traditional methods—maximum likelihood detection (MLD), minimum mean square error (MMSE), and zero-forcing (ZF)—is meticulously evaluated alongside a novel DNN-based approach. The proposed study presents a novel DNN-based signal detection model. While this model demonstrates superior computational efficiency and symbol error rate (SER) performance compared to more conventional techniques like MLD, MMSE, and ZF in the context of a SISO system, MIMO systems face some challenges in outperforming the conventional techniques specifically in terms of computation times. This complexity of MIMO systems presents challenges that the current DNN design has yet to fully address, indicating the need for further developments in wireless communication technology. The observed performance difference between SISO and MIMO systems underscores the need for further research on the adaptability and limitations of DNN architectures in MIMO contexts. These findings pave the way for future explorations of advanced neural network architectures and algorithms specifically designed for MIMO signal-processing tasks. By overcoming the performance gap observed in this work, such advancements hold significant promise for enhancing the effectiveness of DNN-based signal detection in MIMO communication systems. [ABSTRACT FROM AUTHOR]

Accurately predicting the long-term trajectory of a surface drifting buoy (SDB) is challenging. This paper proposes a promising solution to the SDB trajectory prediction based on artificial intelligence (AI) technologies. Initially, a scalable mathematical model for trajectory prediction is developed, transforming the challenge of predicting trajectory points into predicting velocities in eastward and northward directions. Subsequently, a four-layer trajectory prediction calculation framework (FLTPCF) is established, outlining a complete workflow for the real-time online training of marine environment data and SDBs' trajectory prediction. Thirdly, for facilitating accurate long-term trajectory prediction, a hybrid artificial neural network trajectory prediction model, named CNN–BiGRU–Attention, integrates a Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and Attention mechanism (AM), tuned for spatiotemporal feature extraction and extended time-series reasoning. Extensive experiments, including ablation studies, comparative analyses with state-of-the-art models like BiLSTM and Transformer, evaluations against numerical methods, and adaptability tests, were conducted for justifying the CNN–BiGRU–Attention model. The results highlight the CNN–BiGRU–Attention model's excellent convergence, accuracy, and generalization capabilities in predicting 24, 48, and 72 h trajectories for SDBs with varying drogue statuses and under different sea conditions. This work has great potential to promote the intelligent degree of marine environmental monitoring. [ABSTRACT FROM AUTHOR]

The degradation of the cutting tool and its optimal replacement is a major problem in machining given the variability in this degradation even under constant cutting conditions. Therefore, monitoring the degradation of cutting tools is an important part of the process in order to replace the tool at the optimal time and thus reduce operating costs. In this paper, a cutting tool degradation monitoring technique is proposed using bootstrap-based artificial neural networks. Different indicators from the turning operation are used as input to the approach: the RMS value of the cutting force and torque, the machining duration, and the total machined length. They are used by the approach to estimate the size of the flank wear (VB). Different neural networks are tested but the best results are achieved with an architecture containing two hidden layers: the first one containing six neurons with a Tanh activation function and the second one containing six neurons with an ReLu activation function. The novelty of the approach makes it possible, by using the bootstrap approach, to determine a confidence interval around the prediction. The results show that the networks are able to accurately track the degradation and detect the end of life of the cutting tools in a timely manner, but also that the confidence interval allows an estimate of the possible variation of the prediction to be made, thus helping in the decision for optimal tool replacement policies. [ABSTRACT FROM AUTHOR]

Background and objectives: This review aims to delve into the role of artificial intelligence in medicine. Ulcerative colitis (UC) is a chronic, inflammatory bowel disease (IBD) characterized by superficial mucosal inflammation, rectal bleeding, diarrhoea and abdominal pain. By identifying the challenges inherent in UC diagnosis, we seek to highlight the potential impact of artificial intelligence on enhancing both diagnosis and treatment methodologies for this condition. Method: A targeted, non-systematic review of literature relating to ulcerative colitis was undertaken. The PubMed and Scopus databases were searched to categorize a well-rounded understanding of the field of artificial intelligence and its developing role in the diagnosis and treatment of ulcerative colitis. Articles that were thought to be relevant were included. This paper only included articles published in English. Results: Artificial intelligence (AI) refers to computer algorithms capable of learning, problem solving and decision-making. Throughout our review, we highlighted the role and importance of artificial intelligence in modern medicine, emphasizing its role in diagnosis through AI-assisted endoscopies and histology analysis and its enhancements in the treatment of ulcerative colitis. Despite these advances, AI is still hindered due to its current lack of adaptability to real-world scenarios and its difficulty in widespread data availability, which hinders the growth of AI-led data analysis. Conclusions: When considering the potential of artificial intelligence, its ability to enhance patient care from a diagnostic and therapeutic perspective shows signs of promise. For the true utilization of artificial intelligence, some roadblocks must be addressed. The datasets available to AI may not truly reflect the real-world, which would prevent its impact in all clinical scenarios when dealing with a spectrum of patients with different backgrounds and presenting factors. Considering this, the shift in medical diagnostics and therapeutics is coinciding with evolving technology. With a continuous advancement in artificial intelligence programming and a perpetual surge in patient datasets, these networks can be further enhanced and supplemented with a greater cohort, enabling better outcomes and prediction models for the future of modern medicine. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent system for the dynamic estimation of sheep body weight (BW). The methodology used to estimate body weight is based on measuring seven biometric parameters: height at withers, rump height, body length, body diagonal length, total body length, semicircumference of the abdomen, and semicircumference of the girth. A biometric parameter acquisition system was developed using a Kinect as a sensor. The results were contrasted with measurements obtained manually with a flexometer. The comparison gives an average root mean square error (RMSE) of 9.91 and a mean R 2 of 0.81. Subsequently, the parameters were used as input in a back-propagation artificial neural network. Performance tests were performed with different combinations to make the best choice of architecture. In this way, an intelligent body weight estimation system was obtained from biometric parameters, with a 5.8% RMSE in the weight estimations for the best architecture. This approach represents an innovative, feasible, and economical alternative to contribute to decision-making in livestock production systems. [ABSTRACT FROM AUTHOR]

Maritime traffic is increasing more and more, creating more complex navigation environments for ships. Ship trajectory prediction based on historical AIS data is a vital method of reducing navigation risks and enhancing the efficiency of maritime traffic control. At present, employing machine learning or deep learning techniques to construct predictive models based on AIS data has become a focal point in ship trajectory prediction research. This paper systematically evaluates various trajectory prediction methods, spanning classical machine learning approaches and emerging deep learning techniques, to uncover their respective merits and drawbacks. In this work, a variety of studies were investigated that applied different algorithms in ship trajectory prediction, including regression models (RMs), artificial neural networks (ANNs), Kalman filtering (KF), and random forests (RFs) in machine learning, along with deep learning such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), gate recurrent unit (GRU) networks, and sequence-to-sequence (Seq2seq) networks. The performance of predictive models based on different algorithms in trajectory prediction tasks was graded and analyzed. Among the existing studies, deep learning methods exhibit significant performance and considerable potential application value for maritime traffic systems, which can be assessed by future work on ship trajectory prediction research. [ABSTRACT FROM AUTHOR]

One of today's main concerns is to bring artificial intelligence capabilities to embedded systems for edge applications. The hardware resources and power consumption required by state-of-the-art models are incompatible with the constrained environments observed in edge systems, such as IoT nodes and wearable devices. Spiking Neural Networks (SNNs) can represent a solution in this sense: inspired by neuroscience, they reach unparalleled power and resource efficiency when run on dedicated hardware accelerators. However, when designing such accelerators, the amount of choices that can be taken is huge. This paper presents SpikExplorer, a modular and flexible Python tool for hardware-oriented Automatic Design Space Exploration to automate the configuration of FPGA accelerators for SNNs. SpikExplorer enables hardware-centric multiobjective optimization, supporting target factors such as accuracy, area, latency, power, and various combinations during the exploration process. The tool searches the optimal network architecture, neuron model, and internal and training parameters leveraging Bayesian optimization, trying to reach the desired constraints imposed by the user. It allows for a straightforward network configuration, providing the full set of explored points for the user to pick the trade-off that best fits their needs. The potential of SpikExplorer is showcased using three benchmark datasets. It reaches 95.8% accuracy on the MNIST dataset, with a power consumption of 180 mW/image and a latency of 0.12 ms/image, making it a powerful tool for automatically optimizing SNNs. [ABSTRACT FROM AUTHOR]

For over a century, induction furnaces have been used in the core of foundries for metal melting and heating. They provide high melting/heating rates with optimal efficiency. The occurrence of faults not only imposes safety risks but also reduces productivity due to unscheduled shutdowns. The problem of diagnosing faults in induction furnaces has not yet been studied, and this work is the first to propose a data-driven framework for diagnosing faults in this application. This paper presents a deep neural network framework for diagnosing electrical faults by measuring real-time electrical parameters at the supply side. Experimental and sensory measurements are collected from multiple energy analyzer devices installed in the foundry. Next, a semi-supervised learning approach, known as the local outlier factor, has been used to discriminate normal and faulty samples from each other and label the data samples. Then, a deep neural network is trained with the collected labeled samples. The performance of the developed model is compared with several state-of-the-art techniques in terms of various performance metrics. The results demonstrate the superior performance of the selected deep neural network model over other classifiers, with an average F-measure of 0.9187. Due to the black box nature of the constructed neural network, the model predictions are interpreted by Shapley additive explanations and local interpretable model-agnostic explanations. The interpretability analysis reveals that classified faults are closely linked to variations in odd voltage/current harmonics of order 3, 11, 13, and 17, highlighting the critical impact of these parameters on the model's prediction. [ABSTRACT FROM AUTHOR]