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Accurate medical recordkeeping is important for personal and public health. Conventional forms of on‐patient medical information, such as medical alert bracelets or finger‐markings, may compromise patient privacy because they are readily visible to other people. Here, the development of an invisible, temporary, and easily deployable on‐patient medical recordkeeping system is reported. Information is stored in unique patterns of spatially distributed near‐infrared (NIR) fluorescent quantum dots (QDs), which are delivered to the skin using dissolvable microneedle arrays. The patterns are invisible to the naked eye but detectable with an infrared camera, which can extract information with >98% accuracy using automated pattern recognition software. By encapsulating NIR QDs in an FDA‐approved biodegradable polymer, biodegradation rates can be tuned so that the encoded medical information can be conveyed in both a spatial and temporal manner, with some components fading within 100 days and others persisting for 6 months. This may be particularly useful for administering a series of vaccinations or treatments by indicating if enough time has passed for the patient to receive the next dose. Importantly, this system contains no personal information, does not require connection to a centralized database, and is not visible to the naked eye, ensuring patient privacy. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Sensors titled "Artificial Intelligence Methods for Smart Cities." The special issue focuses on the use of artificial intelligence (AI) and the Internet of Things (IoT) in smart cities. The articles in the special issue cover a range of topics, including anomaly detection, traffic management, public safety, surveillance, and indoor surveillance. The articles highlight the potential benefits of AI and IoT in improving urban systems and addressing key urban challenges. The special issue also acknowledges the challenges and ethical considerations associated with the widespread adoption of these technologies. [Extracted from the article]

A Single-valued neutrosophic set (SVNS) has recently been explored as a comprehensive tool to assess uncertain information due to varied human cognition. This notion stretches the domain of application of the classical fuzzy set and its extended versions. Various comparison measures based on SVNSs like distance measure, similarity measure, and, divergence measure have practical significance in the study of clustering analysis, pattern recognition, machine learning, and computer vision-related problems. Existing measures have some drawbacks in terms of precision and exclusion of information and produce unreasonable results in categorization problems. In this paper, we propose a generic method to define new divergence measures based on common aggregation operators and discuss some algebraic properties of the proposed divergence measures. To further appreciate the proposed divergence measures, their application to pattern recognition has been investigated in conjunction with the prominent existing comparison measures based on SVNSs. The comparative assessment sensitivity analysis of the proposed measures establishes their edge over the existing ones because of appropriate classification results. [ABSTRACT FROM AUTHOR]

The domain name system (DNS) protocol has been used for over three decades. It plays a vital role in the functioning of the Internet by facilitating the conversion of domain names into IP addresses. However, DNS is an early and vulnerable network protocol that attackers frequently target due to its numerous security flaws. To address these security concerns, several improvements have been introduced over time. The most recent enhancement is DNS over HTTPS (DoH), which aims to enhance user privacy and security by safeguarding DNS requests and responses from eavesdropping and data manipulation. Nevertheless, DoH encounters several security and privacy issues, such as encrypted traffic hindering network administrators from inspecting DNS packets for Malicious activity. Consequently, this raises concerns regarding potential security breaches and increased risk. Identification and characterizing Malicious behavior of DoH network traffic helps mitigate these threats. To tackle these issues, this research proposes two statistical pattern recognition models based on logistic and linear regression. These proposed models aim to identify the profile of Malicious DoH network traffic behavior by recognizing data patterns. In this order, we proposed two models consisting of two primary stages: data preprocessing, which involves data preparation and the selection of optimal feature sets, and pattern recognition, in which the most suitable pattern is selected and used for data classification. We also presented the obtained Malicious DoH profile utilizing the correlation coefficients between the features. To assess the effectiveness of the proposed approaches, the CIRA-CIC-DoHBrw-2020 dataset is utilized, and a comparison is made against state-of-the-art machine learning and deep learning models. Experimental results indicate that the logistic regression-based model outperformed linear regression-based. Moreover, while the outcomes indicated that the effectiveness of the linear and logistic regression-derived models was lower than particular machine learning and deep learning models, our models employed a smaller set of features than earlier research endeavors. Furthermore, our proposed models offer several advantages over previous models, including low computational complexity, simple implementation, robustness to noise, and reduced data requirements This study is the first to use basic statistical models (linear and logistic regression) to profile Malicious behavior in DoH network traffic. [ABSTRACT FROM AUTHOR]

In recent years, various studies have been conducted on SVMs and their applications in different area. They have been developed significantly in many areas. SVM is one of the most robust classification and regression algorithms that plays a significant role in pattern recognition. However, SVM has not been developed significantly in some areas like large-scale datasets, unbalanced datasets, and multiclass classification. Efficient SVM training in large-scale datasets is of great importance in the big data era. However, as the number of samples increases, the time and memory required to train SVM increase, making SVM impractical even for a medium-sized problem. With the emergence of big data, this problem becomes more significant. This paper presents a novel distributed method for SVM training in which a very small subset of training samples is used for classification, which reduces the problem size and thus the required memory and computational resources. The solution of this problem almost converges to standard SVM. This method includes three steps: first, detecting a subset of distributed training samples, second, creating local models of SVM and obtaining partial vectors, and finally combining the partial vectors and obtaining the global vector and the final model. In addition, the datasets which suffer from unbalanced number of samples and tend to the majority class, the proposed method balances the samples of the two classes and it can be used in unbalanced datasets. The empirical results show that using this method is efficient for large-scale problems. [ABSTRACT FROM AUTHOR]

Many features of leaves, including color, texture, and shape, are used in automated leaf recognition models. The curvature of a leaf is one of the least studied characteristics. This is primarily due to the fact that curvature is not an invariant feature and can fluctuate significantly in a single leaf in its many peripheral positions. The second reason is that if one considers curvature in a pixel-by-pixel manner, it seems entirely inappropriate as a single representative feature. In this study, we focused mostly on curvature and talked about how curvature might be used as a distinguishing feature for identifying leaves. We have provided a step-by-step method for dealing with curvature, starting with the fundamentals like the average curvature of a leaf and working our way up to a fine tuned representation that may be used as a significant feature. To determine whether the new feature is appropriate, we added it to the existing feature sets and fed the data to various standard classification models. The findings show a noticeable improvement in accuracy, demonstrating the usefulness of curvature as a feature. [ABSTRACT FROM AUTHOR]

In recent years, there has been a notable rise in the number of patients afflicted with laryngeal diseases, including cancer, trauma, and other ailments leading to voice loss. Currently, the market is witnessing a pressing demand for medical and healthcare products designed to assist individuals with voice defects, prompting the invention of the artificial throat (AT). This user-friendly device eliminates the need for complex procedures like phonation reconstruction surgery. Therefore, in this review, we will initially give a careful introduction to the intelligent AT, which can act not only as a sound sensor but also as a thin-film sound emitter. Then, the sensing principle to detect sound will be discussed carefully, including capacitive, piezoelectric, electromagnetic, and piezoresistive components employed in the realm of sound sensing. Following this, the development of thermoacoustic theory and different materials made of sound emitters will also be analyzed. After that, various algorithms utilized by the intelligent AT for speech pattern recognition will be reviewed, including some classical algorithms and neural network algorithms. Finally, the outlook, challenge, and conclusion of the intelligent AT will be stated. The intelligent AT presents clear advantages for patients with voice impairments, demonstrating significant social values. [ABSTRACT FROM AUTHOR]

The Bio-Radar is herein presented as a non-contact radar system able to capture vital signs remotely without requiring any physical contact with the subject. In this work, the ability to use the proposed system for emotion recognition is verified by comparing its performance on identifying fear, happiness and a neutral condition, with certified measuring equipment. For this purpose, machine learning algorithms were applied to the respiratory and cardiac signals captured simultaneously by the radar and the referenced contact-based system. Following a multiclass identification strategy, one could conclude that both systems present a comparable performance, where the radar might even outperform under specific conditions. Emotion recognition is possible using a radar system, with an accuracy equal to 99.7% and an F1-score of 99.9%. Thus, we demonstrated that it is perfectly possible to use the Bio-Radar system for this purpose, which is able to be operated remotely, avoiding the subject awareness of being monitored and thus providing more authentic reactions. [ABSTRACT FROM AUTHOR]

Rotating machine fault diagnosis plays a vital role in reducing maintenance costs and preventing accidents. Machine learning (ML) methods and Internet of things (IoT) technologies have been recently introduced into machine fault diagnosis and have generated inspiring results. An ML model with more trainable parameters can typically generate a higher fault diagnostic accuracy. However, the IoT nodes have limited computation and storage resources. How to design an ML model with high accuracy and computational efficiency is still a difficulty and challenge. This work develops an enhanced sparse filtering (ESF) method for mining and fusing the features of the machine signals for fault diagnosis. First, a dimension reduction algorithm is utilized for obtaining the principal components of the vibration signals that are hindered by noises. The distinct features of the principal components are then exploited by using sparse filtering (SF). To reduce the overfitting of the SF model, the L 1/2 norm is applied to regularize the objective function. Finally, the obtained features are combined as the inputs of a softmax classifier for machine fault pattern recognition. The effectiveness, superiority, and robustness of the proposed ESF method are validated by the simulated signals and the practical bearing and motor fault signals compared with the other conventional methods. The lightweight and intelligent ESF algorithm is also deployed onto an edge computing node to realize online motor fault diagnosis. The designed model and the proposed method show great potential in highly accurate and efficient rotation machine fault diagnosis. [ABSTRACT FROM AUTHOR]

With the continuous development of computer technology and the deepening of mathematical theory research, the combination of mathematics and computer pattern recognition technology has penetrated into various fields of scientific research and social production. In order to accurately detect and analyze students' movements in dance teaching, a new optical detection method is proposed in this study, which uses dynamic time distortion algorithm to accurately identify and analyze dance movements. The dynamic time warping algorithm is used to compare two time series and is applied to the detection of dance movements to realize the accurate identification and analysis of students' movements. The dance video data is collected and pre-processed into time series data, and then each time series is compared and analyzed using a dynamic time warping algorithm, and key movement features are extracted. Finally, the machine learning model is trained to classify and evaluate the students' movements. The experimental results show that the optical detection method based on dynamic time distortion algorithm can achieve high accuracy and precision in dance teaching. When applied to dance teaching, it has high accuracy and precision in detecting dance movements, which is expected to provide effective tools and support for dance teaching. [ABSTRACT FROM AUTHOR]

Introduction: The meta-learning methods have been widely used to solve the problem of few-shot learning. Generally, meta-learners are trained on a variety of tasks and then generalized to novel tasks. Methods: However, existing meta-learning methods do not consider the relationship between meta-tasks and novel tasks during the meta-training period, so that initial models of the meta-learner provide less useful meta- knowledge for the novel tasks. This leads to a weak generalization ability on novel tasks. Meanwhile, different initial models contain different meta-knowledge, which leads to certain differences in the learning effect of novel tasks during the-meta-testing period. Therefore, this article puts forward a meta-optimization method based on situational meta-task construction and cooperation of multiple initial models. First, during the meta-training period, a method of constructing situational meta-task is proposed, and the selected candidate task sets provide more effective meta-knowledge for novel tasks. Then, during the meta-testing period, an ensemble model method based on meta-optimization is proposed to minimize the loss of inter-model cooperation in prediction, so that multiple models cooperation can realize the learning of novel tasks. Results: The above-mentioned methods are applied to popular few-shot character datasets and image recognition datasets. Furthermore, the experiment results indicate that the proposed method achieves good effects in few-shot classification tasks. Discussion: In future work, we will extend our methods to provide more generalized and useful meta-knowledge to the model during the meta-training period when the novel few-shot tasks are completely invisible. [ABSTRACT FROM AUTHOR]

This article explores the use of convolutional neural networks (CNN) in studying retinal neural networks. It discusses the application of CNN as a framework for analyzing retinal circuits and examines the relationship between structural parameters and information-processing functions. The article focuses on the primary rod signal pathway in mouse and macaque retinas, particularly electrical synapses. By comparing the differences in retinal circuitry between these species, the author suggests that insights into neurocircuitry principles and adaptational designs can be gained. The article also discusses the methodology for observing neural circuitry using electron microscopy and emphasizes the importance of human pattern recognition in interpreting the images. It concludes by stating that connectomic studies on visual processing can enhance our understanding of natural intelligence. The document includes ethical guidelines, financial support, acknowledgments, a conflict of interest statement, and references to related research papers. [Extracted from the article]

The given text discusses the limitations of feed-forward neural networks in deep learning applications and proposes the use of random vector functional link (RVFL) networks as an alternative. The authors provide theoretical justifications for the effectiveness of RVFL networks in approximating continuous functions on compact domains and extend these results to smooth, compact submanifolds of Euclidean space. They also present numerical experiments to illustrate their findings. The text discusses the theoretical results on approximating properties of RVFL networks, the Monte Carlo approximation method and its application to smooth manifolds, the proof of a theorem regarding the non-asymptotic result for RVFL networks, the use of RVFL networks for approximating functions on manifolds, the construction of RVFL networks for approximating functions on manifolds, an algorithm for approximating a function using RVFL networks, and mathematical equations and proofs related to Fourier transforms and convolution. The authors suggest further research directions and provide a list of references to related research papers and articles. [Extracted from the article]

Education is crucial for a productive life and providing necessary resources. With the advent of technology like artificial intelligence, higher education institutions are incorporating technology into traditional teaching methods. Predicting academic success has gained interest in education as a strong academic record improves a university's ranking and increases student employment opportunities. Modern learning institutions face challenges in analyzing performance, providing high-quality education, formulating strategies for evaluating students' performance, and identifying future needs. E-learning is a rapidly growing and advanced form of education, where students enroll in online courses. Platforms like Intelligent Tutoring Systems (ITS), learning management systems (LMS), and massive open online courses (MOOC) use educational data mining (EDM) to develop automatic grading systems, recommenders, and adaptative systems. However, e-learning is still considered a challenging learning environment due to the lack of direct interaction between students and course instructors. Machine learning (ML) is used in developing adaptive intelligent systems that can perform complex tasks beyond human abilities. Some areas of applications of ML algorithms include cluster analysis, pattern recognition, image processing, natural language processing, and medical diagnostics. In this research work, K-means, a clustering data mining technique using Davies' Bouldin method, obtains clusters to find important features affecting students' performance. The study found that the SVM algorithm had the best prediction results after parameter adjustment, with a 96% accuracy rate. In this paper, the researchers have examined the functions of the Support Vector Machine, Decision Tree, naive Bayes, and KNN classifiers. The outcomes of parameter adjustment greatly increased the accuracy of the four prediction models. Naïve Bayes model's prediction accuracy is the lowest when compared to other prediction methods, as it assumes a strong independent relationship between features. [ABSTRACT FROM AUTHOR]

Handwritten character recognition is one of the classical problems in the field of image classification. Supervised learning techniques using deep learning models are highly effective in their application to handwritten character recognition. However, they require a large dataset of labeled samples to achieve good accuracies. Recent supervised learning techniques for Kannada handwritten character recognition have state of the art accuracy and perform well over a large range of input variations. In this work, a framework is proposed for the Kannada language that incorporates techniques from semi-supervised learning. The framework uses features extracted from a convolutional neural network backbone and uses regularization to improve the trained features and label propagation to classify previously unseen characters. The episodic learning framework is used to validate the framework. Twenty-four classes are used for pre-training, 12 classes are used for testing and 11 classes are used for validation. Fine-tuning is tested using one example per unseen class and five examples per unseen class. Through experimentation the components of the network are implemented in Python using the Pytorch library. It is shown that the accuracy obtained 99.13% make this framework competitive with the currently available supervised learning counterparts, despite the large reduction in the number of labeled samples available for the novel classes. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy management applications by enabling precise monitoring of individual appliance energy consumption without the requirements of intrusive sensors or smart meters. In this technique, the load disaggregation for the individual device is accrued by the recognition of their current signals by employing machine learning (ML) methods. This research paper conducts a comprehensive comparative analysis of various ML techniques applied to NILM, aiming to identify the most effective methodologies for accurate load disaggregation. The study employs a diverse dataset comprising high-resolution electricity consumption data collected from an Estonian household. The ML algorithms, including deep neural networks based on long short-term memory networks (LSTM), extreme gradient boost (XgBoost), logistic regression (LR), and dynamic time warping with K-nearest neighbor (DTW-KNN) are implemented and evaluated for their performance in load disaggregation. Key evaluation metrics such as accuracy, precision, recall, and F1 score are utilized to assess the effectiveness of each technique in capturing the nuanced energy consumption patterns of diverse appliances. Results indicate that the XgBoost-based model demonstrates superior performance in accurately identifying and disaggregating individual loads from aggregated energy consumption data. Insights derived from this research contribute to the optimization of NILM techniques for real-world applications, facilitating enhanced energy efficiency and informed decision-making in smart grid environments. [ABSTRACT FROM AUTHOR]

This article discusses the use of artificial intelligence (AI) in cardiovascular imaging and provides a framework for assessing the quality and clinical value of AI applications in this field. The article explains the definitions of AI, machine learning (ML), and deep learning (DL), and discusses their roles in cardiology. It emphasizes the importance of high-quality input data, addressing bias, and performing appropriate external validation. The article also highlights the need for interpretability and explainability in AI algorithms, the importance of addressing bias and promoting equity, and the evolving scientific, ethical, and regulatory landscape surrounding AI in healthcare. The authors suggest that a synergistic deployment of AI tools and trained professionals can benefit patient care, but caution against the misuse of AI. [Extracted from the article]

Heart disease (HD) accounts for more deaths every year than other illnesses. World Health Organization (WHO) assessed 17.9 million life losses caused by heart disease in 2016, demonstrating 31% of all international life losses. Three-quarters of these life losses occur in low and middle-income nations. Machine learning (ML), due to advanced precision in pattern recognition and classification, demonstrates to be in effect in complementing decision-making and threat prediction from the huge number of HD data created by the healthcare sector. Thus, this study aims to develop a logistic regression model (LRM) for predicting the risk of getting HD in ten years. The study explores the different methodologies for improving the performance of base LRM for predicting whether a person gets HD after ten years or not. The result demonstrates the capability of LRM in predicting the risks of getting HD after ten years. The LRM achieves 97.35% accuracy with the recursive feature elimination and random under-sampling. This implies that the LRM can play an important role in precautionary methods to avoid the risk of HD. [ABSTRACT FROM AUTHOR]

In the context of escalating energy demands and the quest for sustainable waste management solutions, this paper evaluates the efficacy of three machine learning methods—ElasticNet, Decision Trees, and Neural Networks—in predicting energy recovery from municipal waste across the European Union. As renewable energy sources increasingly dominate the energy production landscape, the integration of Waste-to-Energy (WTE) processes presents a dual advantage: enhancing waste management and contributing to the renewable energy mix. This study leverages a dataset incorporating economic and environmental indicators from 25 European countries, spanning 2013–2020, to compare the predictive capabilities of the three machine learning models. The analysis reveals that Neural Networks, with their intricate pattern recognition capabilities, outperform ElasticNet and Decision Trees in predicting energy recovery metrics, as evidenced by superior performance in key statistical indicators such as R-value, Mean Squared Error (MSE), and Mean Absolute Error (MAE). The comparative analysis not only demonstrates the effectiveness of each method but also suggests Neural Networks as a pivotal tool for informed decision-making in waste management and energy policy formulation. Through this investigation, the paper contributes to the sustainable energy and waste management discourse, emphasizing the critical intersection of advanced technologies, policy considerations, and environmental stewardship in addressing contemporary energy challenges. [ABSTRACT FROM AUTHOR]

The construction of complex networks from electroencephalography (EEG) proves to be an effective method for representing emotion patterns in affection computing as it offers rich spatiotemporal EEG features associated with brain emotions. In this paper, we propose a novel method for constructing complex networks from EEG signals for emotion recognition, which begins with phase space reconstruction to obtain ordinal patterns and subsequently forms a graph network representation from the sequence of ordinal patterns based on the visibility graph method, named ComNet-PSR-VG. For the proposed ComNet-PSR-VG, the initial step involves mapping EEG signals into a series of ordinal partitions using phase space reconstruction, generating a sequence of ordinal patterns. These ordinal patterns are then quantified to form a symbolized new sequence. Subsequently, the resulting symbolized sequence of ordinal patterns is transformed into a graph network using the visibility graph method. Two types of network node measures, average node degree (AND) and node degree entropy (NDE), are extracted from the graph networks as the inputs of machine learning for EEG emotion recognition. To evaluate the effectiveness of the proposed construction method of complex networks based on the visibility graph of ordinal patterns, comparative experiments are conducted using two types of simulated signals (random and Lorenz signals). Subsequently, EEG emotion recognition is performed on the SEED EEG emotion dataset. The experimental results show that, with AND as the feature, our proposed method is 4.88% higher than the existing visibility graph method and 12.23% higher than the phase space reconstruction method. These findings indicate that our proposed novel method for constructing complex networks from EEG signals not only achieves effective emotional EEG pattern recognition but also exhibits the potential for extension to other EEG pattern learning tasks, suggesting broad adaptability and application potential for our method. [ABSTRACT FROM AUTHOR]

Deep learning algorithms are widely used for pattern recognition in electronic noses, which are sensor arrays for gas mixtures. One of the challenges of using electronic noses is sensor drift, which can degrade the accuracy of the system over time, even if it is initially trained to accurately estimate concentrations from sensor data. In this paper, an effective drift compensation method is introduced that adds sensor drift information during training of a neural network that estimates gas concentrations. This is achieved by concatenating a calibration feature vector with sensor data and using this as an input to the neural network. The calibration feature vector is generated via a masked-autoencoder-based feature extractor trained with transfer samples, and acts as a prompt to convey sensor drift information. Our method is tested on a 3-year gas sensor array drift dataset, showing that a neural network using our method performs better than other models, including a network with additional fine tuning, demonstrating that our method is efficient at compensating for sensor drift. In this study, the effectiveness of using prompts for network training is confirmed, which better compensates for drifts in new sensor signals than network fine-tuning. [ABSTRACT FROM AUTHOR]

This document provides an overview of recent advancements in audio signal processing. The use of artificial intelligence (AI) and deep learning technologies has greatly improved the analysis and interpretation of audio signals. These advancements have led to progress in areas such as speaker recognition, sound categorization, emotional computing, and noise reduction. The document highlights several research articles that explore different aspects of audio signal processing, including the effects of noise on gas discharges, deep learning-based detection of voice pathology (specifically COVID-19), estimating the impact of room acoustics on sound signals, analyzing the performance of vacuum cleaners based on sound analysis, incomplete sound event detection, analyzing dolphin whistles, high-speed pitch detection, acoustic imaging for spatially mapping sounds, enhancing personal audio systems with sound field control technology, and audio classification in construction sites. These advancements demonstrate the potential of audio processing and analysis techniques in various fields. [Extracted from the article]

The stable operation of a power supply system is inseparable from the work of detecting defects in transmission lines. However, the insulator defect detection model based on deep learning is widely used in wire inspection work. Therefore, this paper proposes an improved YOLOv5s insulator defect detection model in order to solve the problems of insufficient training data and low recognition accuracy of the target detection model in the real-time detection of small target insulator defects. To expand and enhance the training data, experiments were conducted using the addition of noise and random black blocks. The spatial and channel weight coefficients were obtained by adding an attention mechanism (Convolutional Block Attention Module, CBAM), and the dimensions of the input feature maps were transformed to enhance the model's ability to extract and fuse small target defect features. Experiments show that with Faster RCNN, YOLOv3, SSD and YOLOv4 comparison experiments verified that the algorithm achieves 97.38% detection accuracy for insulators and 93.32% detection accuracy for small target insulator defects with a fast detection speed, which is a better solution to the problem of detecting insulator defects with too small a proportion in the image. [ABSTRACT FROM AUTHOR]

Groundwater level (GWL) dynamics result from a complex interplay between groundwater systems and the Earth system. This study aims to identify common hydrogeological patterns and to gain a deeper understanding of the underlying similarities and their link to physiographic, climatic, and anthropogenic controls of groundwater in coastal regions. The most striking aspects of GWL dynamics and their controls were identified through a combination of statistical metrics, calculated from about 8000 groundwater hydrographs, pattern recognition using clustering algorithms, classification using random forest, and SHapley Additive exPlanations (SHAPs). Hydrogeological similarity was defined by four clusters representing distinct patterns of GWL dynamics. These clusters can be observed globally across different continents and climate zones but simultaneously vary regionally and locally, suggesting a complicated interplay of controlling factors. The main controls differentiating GWL dynamics were identified, but we also provide evidence for the currently limited ability to explain GWL dynamics on large spatial scales, which we attribute mainly to uncertainties in the explanatory data. Finally, this study provides guidance for systematic and holistic groundwater monitoring and modeling and motivates a consideration of the different aspects of GWL dynamics, for example, when predicting climate-induced GWL changes, and the use of explainable machine learning techniques to deal with GWL complexity – especially when information on potential controls is limited or needs to be verified. [ABSTRACT FROM AUTHOR]

Intrusion detection is a key topic in computer security. Due to the ever-increasing number of network attacks, several accurate anomaly-based techniques have been proposed for intrusion detection, wherein pattern recognition through machine learning techniques is typically used. Many proposals rely on the use of autoencoders, due to their capability to analyze complex, high-dimensional, and large-scale data. They capitalize on composite architectures and accurate learning approaches, possibly in combination with sophisticated feature selection techniques. However, due to their high complexity and lack of transferability of the impressive intrusion detection results, they are hardly ever used in production environments. This paper is developed around the intuition that complexity is not necessarily justified because a single autoencoder is enough to obtain similar, if not better, intrusion detection results compared to related proposals. The wide study presented here addresses the effect of the seed, a deep investigation on the training loss, and feature selection across the use of different hardware platforms. The best practices presented, regarding set-up and training, threshold setting, and possible use of feature selection techniques for performance improvement, can be valuable for any future work on the use of autoencoders for successful intrusion detection purposes. [ABSTRACT FROM AUTHOR]

After 2010, the consumer price index fell to a low level in the EU. In the euro area, it remained low between 2010 and 2020. The European Central Bank has even had to take action against the emergence of deflation. The situation changed significantly in 2021. Inflation jumped to levels not seen for 40 years in the EU. Our study aims to use artificial intelligence to forecast inflation. We also use artificial intelligence to forecast stock index changes. Based on the forecasts, we propose portfolio reallocation decisions to protect against inflation. The forecasting literature does not address the importance of structural breaks in the time series, which, among other things, can affect both the pattern recognition and prediction capabilities of various machine learning models. The novelty of our study is that we used the Zivot–Andrews unit root test to determine the breakpoints and partitioned the time series into training and testing datasets along these points. We then examined which database partition gives the most accurate prediction. This information can be used to re-balance the portfolio. Two different AI-based prediction algorithms were used (GRU and LSTM), and a hybrid model (LSTM–GRU) was also included to investigate the predictability of inflation. Our results suggest that the average error of the inflation forecast is a quarter of that of the stock market index forecast. Inflation developments have a fundamental impact on equity and government bond returns. If we obtain a reliable estimate of the inflation forecast, we have time to rebalance the portfolio until the inflation shock is incorporated into government bond returns. Our results not only support investment decisions at the national economy level but are also useful in the process of rebalancing international portfolios. [ABSTRACT FROM AUTHOR]

The principal component analysis (PCA) is widely used in computational science branches such as computer science, pattern recognition, and machine learning, as it can effectively reduce the dimensionality of high-dimensional data. In particular, it is a popular transformation method used for feature extraction. In this study, we explore PCA's ability for feature selection in regression applications. We introduce a new approach using PCA, called Targeted PCA to analyze a multivariate dataset that includes the dependent variable—it identifies the principal component with a high representation of the dependent variable and then examines the selected principal component to capture and rank the contribution of the non-dependent variables. The study also compares the feature selected with that resulting from a Least Absolute Shrinkage and Selection Operator (LASSO) regression. Finally, the selected features were tested in two regression models: multiple linear regression (MLR) and artificial neural network (ANN). The results are presented for three socioeconomic, environmental, and computer image processing datasets. Our study found that 2 of 3 random datasets have more than 50% similarity in the selected features by the PCA and LASSO regression methods. In the regression predictions, our PCA-selected features resulted in little difference compared to the LASSO regression-selected features in terms of the MLR prediction accuracy. However, the ANN regression demonstrated a faster convergence and a higher reduction of error. [ABSTRACT FROM AUTHOR]

This study addresses the critical issue of classifying cardiac ischemia, a disease with signifi- cant global health implications that contributes to the global mortality rate. In our study, we tackle the classification of ischemia using six diverse electrocardiogram (ECG) datasets and a convolutional neural network (CNN) as the primary methodology. We combined six separate datasets to gain a more comprehensive understanding of cardiac electrical activity, utilizing 12 leads to obtain a broader perspective. A discrete wavelet transform (DWT) preprocessing was used to eliminate irrelevant information from the signals, aiming to improve classification results. Focusing on accuracy and minimizing false negatives (FN) in ischemia detection, we enhance our study by incorporating various machine learning models into our base model. These models include multilayer perceptron (MLP), support vector machines (SVM), random forest (RF), long short-term memory (LSTM), and bidirectional LSTM (BiLSTM), allowing us to leverage the strengths of each algorithm. The CNN-BiLSTM model achieved the highest accuracy of 99.23% and demonstrated good sensitivity of 98.53%, effectively reducing false negative cases in the overall tests. The CNN-BiLSTM model demonstrated the ability to effectively identify abnormalities, misclassifying only 25 out of 1,673 ischemic cases in the test set as normal. This is due to the BiLSTM's efficiency in capturing long-range dependencies and sequential patterns, making it suitable for tasks involving time-series data such as ECG signals. In addition, CNNs are well-suited for hierarchical feature learning and complex pattern recognition in ECG data. [ABSTRACT FROM AUTHOR]

Recently, graph-based semi-supervised learning (GSSL) has garnered significant interest in the realms of machine learning and pattern recognition. Although some of the proposed methods have made some progress, there are still some shortcomings that need to be overcome. There are three main limitations. First, the graphs used in these approaches are usually predefined regardless of the task at hand. Second, due to the use of graphs, almost all approaches are unable to process and consider data with a very large number of unlabeled samples. Thirdly, the imbalance of the topology of the samples is very often not taken into account. In particular, processing large datasets with GSSL might pose challenges in terms of computational resource feasibility. In this article, we present a scalable and inductive GSSL method. We broaden the scope of the graph topology imbalance paradigm to extensive databases. Second, we employ the calculated weights of the labeled sample for the label-matching term in the global objective function. This leads to a unified, scalable, semi-supervised learning model that allows simultaneous labeling of unlabeled data, projection of the feature space onto the labeling space, along with the graph matrix of anchors. In the proposed scheme, the integration of labels and features from anchors is applied for the adaptive construction of the anchor graph. Experimental results were performed on four large databases: NORB, RCV1, Covtype, and MNIST. These experiments demonstrate that the proposed method exhibits superior performance when compared to existing scalable semi-supervised learning models. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PdM) is currently the most cost-effective maintenance method for industrial equipment, offering improved safety and availability of mechanical assets. A crucial component of PdM is the remaining useful life (RUL) prediction for machines, which has garnered increasing attention. With the rapid advancements in industrial internet of things and artificial intelligence technologies, RUL prediction methods, particularly those based on pattern recognition (PR) technology, have made significant progress. However, a comprehensive review that systematically analyzes and summarizes these state-of-the-art PR-based prognostic methods is currently lacking. To address this gap, this paper presents a comprehensive review of PR-based RUL prediction methods. Firstly, it summarizes commonly used evaluation indicators based on accuracy metrics, prediction confidence metrics, and prediction stability metrics. Secondly, it provides a comprehensive analysis of typical machine learning methods and deep learning networks employed in RUL prediction. Furthermore, it delves into cutting-edge techniques, including advanced network models and frontier learning theories in RUL prediction. Finally, the paper concludes by discussing the current main challenges and prospects in the field. The intended audience of this article includes practitioners and researchers involved in machinery PdM, aiming to provide them with essential foundational knowledge and a technical overview of the subject matter. [ABSTRACT FROM AUTHOR]

Recent advancements in ubiquitous computing have emphasized the need for privacy-preserving occupancy detection in smart environments to enhance security. This work presents a novel occupancy detection solution utilizing privacy-aware sensing technologies. The solution analyzes time-series data to detect not only occupancy as a binary problem, but also determines whether one or multiple individuals are present in an indoor environment. On three real-world datasets, our models outperformed various state-of-the-art algorithms, achieving F1-scores up to 94.91% in single-occupancy detection and a macro F1-score of 91.55% in multi-occupancy detection. This makes our approach a promising solution for improving security in smart environments. [ABSTRACT FROM AUTHOR]

Artificial synapses have gained great interest in the past few years because of their importance in deep learning and image recognition. To fulfill device miniaturization and reduce the energy consumption of device operation, a series of silane‐based photoactive/conjugated self‐assembled molecules (CSAMs), including isoindigo (IID), diketopyrrolopyrrole, and benzodithiophene, are used as the charge‐trapping layers in synaptic phototransistors. The devices comprising CSAM demonstrate excellent short‐term/long‐term memory behaviors and can emulate the paired‐pulse facilitation (PPF) function. Notably, the IID‐based device shows the highest photoresponse, and this performance is highly related to the charge transfer efficiency and the photophysics lifetimes derived from the time‐resolved photoluminescence and the transient absorption characterizations. Therefore, IID produces the highest PPF ratios of 139% to blue light and 144% to green light. In addition, the energy consumption of 0.029 fJ at an operating voltage of −0.1 mV is achieved, which is the lowest value in synaptic phototransistors so far. Notably, neural networks of supervised and unsupervised learning algorithms are demonstrated in the device studied to process a pattern recognition system. Collectively, using conjugated self‐assembled materials as a charge‐trapping layer is a promising way for synaptic phototransistor applications to reduce energy consumption and fulfill the device miniaturization. [ABSTRACT FROM AUTHOR]

Part I reviews persistent challenges obstructing progress in understanding complex fatigue's biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades. [ABSTRACT FROM AUTHOR]

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration. [ABSTRACT FROM AUTHOR]

The objects of the study are unmanned vehicles and branches of the bridge of the city of Kyiv (Ukraine), which connects the Great Ring Road, Zhytomyr Highway and Peremogy Avenue. The built routes were analyzed using the technology of recognition of road signs, people and vehicles. The important problem of this research is to analyze the possibilities of detecting obstacles by an unmanned vehicle using pattern recognition, which combines the methods of machine communication, navigation and real-time video surveillance. Based on the study, the results of detecting and avoiding obstacles on the road, where a study was conducted to investigate the main reasons that can cause time delays (traffic jams, weather conditions, accidents). The results of planning and navigation are obtained to determine the appropriate road route, which allows detecting and eliminating obstacles on the road, as well as building a map plan of the route in advance using online map services (Google Maps). It is shown that recognition of road signs (based on the classification using a road sign map consisting of 7 categories), people and vehicles minimizes the occurrence of road accidents, traffic jams and time delays. To recognize the images of road signs, people and vehicles, we studied the road sections connecting to the branched bridge. Thus, the authors have reviewed and analyzed the digital capabilities of pattern identification and recognition using machine learning methods, navigation and video surveillance systems, where the safety of vehicles with detection of road signs and obstacles on the way is of great importance. The results obtained can complement the possibilities of using unmanned vehicles to avoid obstacles and road accidents based on a trained pattern recognition system. This system, using convolutional neural networks and video surveillance navigation systems, will be able to provide the driver and the people around it with safe driving conditions. [ABSTRACT FROM AUTHOR]

Highway bridges stand as paramount elements within transportation infrastructure systems. The ability to ensure swift recovery after extreme events, such as earthquakes, is a fundamental trait of resilient communities. Consequently, expediting the recovery process necessitates near real-time diagnosis of structural damage to provide dependable information. In this study, a data-driven approach for damage detection and assessment is investigated, focusing on bridge columns—the pivotal supporting elements of bridge systems—based on simulations derived from nonlinear time history analysis. This research introduces a set of cumulative intensity-based damage features, whose efficacy is demonstrated through unsupervised learning techniques. Leveraging the support vector machine, a prominent pattern recognition algorithm in supervised learning, alongside Bayesian optimization with a Gaussian process, seismic damage detection and assessment are explored. Encouragingly, the methodology yields high estimation accuracies for both binary outcomes (indicating the presence of damage or the occurrence of collapse) and multi-class classifications (indicating the severity of damage). This breakthrough opens avenues for the practical implementation of on-board sensor computing, enabling near real-time damage detection and assessment in bridge structures. [ABSTRACT FROM AUTHOR]

Hourly solar radiation (SR) forecasting is a vital stage in the efficient deployment of solar energy management systems. Single and hybrid machine learning (ML) models have been predominantly applied for precise hourly SR predictions based on the pattern recognition of historical heterogeneous weather data. However, the integration of ML models has not been fully investigated in terms of overcoming irregularities in weather data that may degrade the forecasting accuracy. This study investigated a strategy that highlights interactions that may exist between aggregated prediction values. In the first investigation stage, a comparative analysis was conducted utilizing three different ML models including support vector machine (SVM) regression, long short-term memory (LSTM), and multilayer artificial neural networks (MLANN) to provide insights into their relative strengths and weaknesses for SR forecasting. The comparison showed the proposed LSTM model had the greatest contribution to the overall prediction of six different SR profiles from numerous sites in Morocco. To validate the stability of the proposed LSTM, Taylor diagrams, violin plots, and Kruskal–Wallis (KW) tests were also utilized to determine the robustness of the model's performance. Secondly, the analysis found coupling the models outputs with aggregation techniques can significantly improve the forecasting accuracy. Accordingly, a novel aggerated model that integrates the forecasting outputs of LSTM, SVM, MLANN with Sugeno λ-measure and Sugeno integral named (SLSM) was proposed. The proposed SLSM provides spatially and temporary interactions of information that are characterized by uncertainty, emphasizing the importance of the aggregation function in mitigating irregularities associated with SR data and achieving an hourly time scale forecasting accuracy with improvement of 11.7 W/m2. [ABSTRACT FROM AUTHOR]

This paper constructs a dataset of handwritten Indus signs employing a social approach. A writing system called the Indus script was created in the Indus civilization. It has been decoded numerous times throughout the years, but it has not yet been fully deciphered. Due to a lack of information and the scarcity of evidence, the mystery of the Indus signs has not yet been fully solved. Recently, there has been an increase in demand for huge datasets in order to use cutting-edge machine learning techniques. Considering the restricted availability of images of authentic Indus signs, this paper proposes creating an Indus signs dataset by asking participants to draw the Indus signs while referring to the image of the original Indus signs. A web application was developed and used to collect the 44 participants' handwritten images of ten Indus signs. To show the availability of the constructed dataset, it is used to train convolutional neural networks. The experimental result demonstrates that the model can classify the images of original Indus script with 70% accuracy. [ABSTRACT FROM AUTHOR]

To preserve Chinese cultural heritage, the originality and complexity of calligraphy characters are proof of the country's unique literary heritage. However, it has long been challenging to comprehend and appropriately classify these complex characters. The absence of a quantitative standard for calligraphy Chinese character recognition has limited accurate assessments and recognition, allowing room for improvement. Therefore, this article seeks to improve the digital evolution of Chinese calligraphy and painting works by leveraging the quick development of computer-aided technology and deep learning algorithms. We collected Chinese calligraphy samples and refined them through digitization, preprocessing, noise reduction, and resizing. We used the HOG approach to identify the unique features of each character and the Euler distance to measure spatial relationships between target and background points, capturing their distinct strokes and patterns. Then, we employed the Google LeNet Inception-v3 model to take advantage of the Convolutional Neural Network's (CNN) capability. Our system can reliably recognize and categorize different calligraphy styles thanks to our CNN-based methodology, going beyond the constraints of conventional recognition techniques. Finally, we carefully evaluated the precision, recall, and accuracy, recognition capacity of our proposed recognition system to assess its effectiveness in correctly identifying calligraphy Chinese characters. The outcomes of our thorough analysis show a recognition rate of 93.12%, illuminating the tremendous potential of our strategy. Our method regularly beats competing algorithms, even in the presence of Gaussian white noise, obtaining accuracy rates of 91.3%, 90.9%, and 89.4% for noise levels of 0.02, 0.04, and 0.06, respectively. [ABSTRACT FROM AUTHOR]

In this study, surface roughness is analyzed within a supervised pattern recognition framework using machine learning methods to present a robust technique for the quantitative determination of surface roughness of rocks. To reach this goal, rock surfaces are classified based on statistical, fractal, geostatistical, directional, and spectral features obtained from the surface profiles as well as the results of direct shear tests. In this way, after the calculation of the features in more than 9000 profiles collected from 84 natural rock samples, a representative vector containing representative features of the surface profiles was introduced for each surface, and joint roughness coefficient (JRC) was back-calculated from 84 direct shear tests on the rock samples. Then, principal component analysis and linear discriminant analysis of the representative vectors were performed in order to prepare the inputs for the classification. Multilayer perceptron (MLP) and radial basis function (RBF) artificial neural networks and support vector machine (SVM) were trained for the classification of rock surfaces. On the other hand, convolutional neural network (CNN) in which features are automatically extracted from the image data of representative surface profiles was the other method for the classification of rock surfaces. The comparison of the results shows RBF is a robust and reliable classifier yielding the least classification error among the investigated methods. Furthermore, comparing the results of machine learning methods with those of traditional equations proves that the SVM, MLP, and RBF yield superior performance. [ABSTRACT FROM AUTHOR]

Neuromorphic photonics is a cutting-edge fusion of neuroscience-inspired computing and photonics technology to overcome the constraints of conventional computing architectures. Its significance lies in the potential to transform information processing by mimicking the parallelism and efficiency of the human brain. Using optics and photonics principles, neuromorphic devices can execute intricate computations swiftly and with impressive energy efficiency. This innovation holds promise for advancing artificial intelligence and machine learning while addressing the limitations of traditional silicon-based computing. Neuromorphic photonics could herald a new era of computing that is more potent and draws inspiration from cognitive processes, leading to advancements in robotics, pattern recognition, and advanced data processing. This paper reviews the recent developments in neuromorphic photonic integrated circuits, applications, and current challenges. [ABSTRACT FROM AUTHOR]

Aeutrosophic ranking studies are an important part of medicine that determines the ranks of tests, risk factors, attributes, and medical suppliers. Neutrosophic clustering in healthcare can split data into groups (called clusters) to determine usage patterns for purposes, in which objects within the same cluster have similar properties and objects of different clusters have different properties. Like neutrosophic clustering, neutrosophic classification studies are also a data mining technique. Neutrosophic pattern recognition is a machine learning process to decipher the underlying patterns in the concerned subjects. Neutrosophic time series analysis tries to find patterns and rules depending on time, neutrosophic recognition of medical images belongs to this type of study. This article comes as an attempt to review and shed light on almost all studies and subjects that used neutrosophic studies and algorithms related to medicine and healthcare for dozens of articles and authors. [ABSTRACT FROM AUTHOR]

Recommendation system and classification machine learning techniques have emerged as a major area of investigation into the issue of information overload. Software tools called "recommender systems" aim to help users make informed choices about services. This paper developed a low-dimensional embedding in which a limited number of features can be used to represent the data object. For the purpose of acquiring the low rank latent factors, matrix factorization techniques have received a lot of attention. The proposed model is stated as the Collaborative Filtering Machine Learning (CFML) for the multi-label classification are focused. In most extreme edge lattice factorization plan of cooperative sifting, appraisals framework with different discrete qualities is treated by uncommonly st retching out pivot misfortune capability to suit numerous levels. The comparative analysis is performed for the two-class classifier into a single multiclass classifier. Alternately, multiple two-class classifiers can be arranged in a hierarchical fashion to create a multi-class classifier. To deal with the ordinal rating matrix's completion. The performance of the CFML model is effective for the recommendation system design. Through automatic feature extraction and pattern recognition, deep learning models excel at capturing intricate relationships and latent factors that underlie user preferences. The paper discusses the multifaceted considerations that these systems can incorporate, such as genre, cast, director, viewer ratings, and individual viewing history. Simulation analysis examine the impact of deep learning-based movie recommenders on streaming platforms and the entertainment industry, demonstrating their ability to not only suggest movies but to curate a personalized journey through the world of cinema. [ABSTRACT FROM AUTHOR]

The evaluation of scientific journals poses challenges owing to the existence of various impact measures. This is because journal ranking is a multidimensional construct that may not be assessed effectively using a single metric such as an impact factor. A few studies have proposed an ensemble of metrics to prevent the bias induced by an individual metric. In this study, a multi-metric journal ranking method based on the standardized average index (SA index) was adopted to develop an extended standardized average index (ESA index). The ESA index utilizes six metrics: the CiteScore, Source Normalized Impact per Paper (SNIP), SCImago Journal Rank (SJR), Hirsh index (H-index), Eigenfactor Score, and Journal Impact Factor from three well-known databases (Scopus, SCImago Journal & Country Rank, and Web of Science). Experiments were conducted in two computer science subject areas: (1) artificial intelligence and (2) computer vision and pattern recognition. Comparing the results of the multi-metric-based journal ranking system with the SA index, it was demonstrated that the multi-metric ESA index exhibited high correlation with all other indicators and significantly outperformed the SA index. To further evaluate the performance of the model and determine the aggregate impact of bibliometric indices with the ESA index, we employed unsupervised machine learning techniques such as clustering coupled with principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE). These techniques were utilized to measure the clustering impact of various bibliometric indicators on both the complete set of bibliometric features and the reduced set of features. Furthermore, the results of the ESA index were compared with those of other ranking systems, including the internationally recognized Scopus, SJR, and HEC Journal Recognition System (HJRS) used in Pakistan. These comparisons demonstrated that the multi-metric-based ESA index can serve as a valuable reference for publishers, journal editors, researchers, policymakers, librarians, and practitioners in journal selection, decision making, and professional assessment. [ABSTRACT FROM AUTHOR]