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The selection of papers on a specified topic requires not only analysis of the vocabulary of every paper on the topic of interest to an end user for relevance, but also taking into account the ultimate goal of the user as such (i.e., what exactly is the problem for which this selection is made to find a solution?). The formation of such a selection on the basis of the occurrence of words in the text of the analyzed paper from the abstracts of papers selected by an expert makes it possible to take into account not only the terms, but also the narration language, which is important, in particular, for the preparation of e-learning courses. The submitted paper is devoted to the relevant problem of estimating the quality of the selection thus formed and the explanation of the solutions obtained. For a document added to the selection, it is proposed to estimate the sentences incorporated into it cumulatively by the share of words with nonzero TF measure values (term frequency), the length of a sentence, and the sum of nonzero values for the term frequency of words incorporated into it. The sentences selected by the criterion of a maximum for the mentioned parameters are used to construct an essay, which is further evaluated for originality with respect to each abstract from the collection formed by an expert and for semantic connectedness in the sentences incorporated into it (with the use of neural network models of the BERT family). Based on the totality of these two estimations, a conclusion on the significance is made for each abstract selected by an expert when including the paper analyzed into the selection. In this case, the generated essay is also used to explain the obtained solution. [ABSTRACT FROM AUTHOR]

The main scientific results of the 16th International Conference on Pattern Recognition and Information Processing (PRIP-2023), Minsk, Republic of Belarus, October 2023, are reviewed and analyzed. The history of this series of conferences is outlined, and its significant role in the development of the theory and practice of image analysis, pattern recognition, and artificial intelligence is indicated. A list of articles in the special issue is provided, prepared from reports selected by the PRIP-2023 Program Committee. [ABSTRACT FROM AUTHOR]

Patterns are crucial for efficiently scheduling microservice workflow applications to containers in cloud computing scenarios. However, it is challenging to learn patterns of microservice workflows because of their complex precedence constrained structures provided by users with more lightweighted, diversified, and personalized services. In this paper, we propose a graph neural network is designed to identify patterns within a set of microservice workflows by mining the common substructures of workflows. Based on the learned patterns, a pattern-based scheduling algorithm framework is developed for microservice workflows with soft deadline constraints to minimize the average tardiness. A sorting strategy is introduced based on urgency and pattern coverage rate. For simplification of the task sorting process, the pattern-based task sorting algorithm (PB-TS) is devised. Furthermore, a resource selection phase is incorporated to the pattern-based resource selection algorithm (PB-RS) to minimize the candidate resource space. Experimental results demonstrate the proposed method is much efficient as compared to three classical algorithms. [ABSTRACT FROM AUTHOR]

Belief and plausibility functions based on evidence theory (ET) have been widely used in managing uncertainty. Various generalizations of ET to fuzzy sets (FSs) have been reported in the literature, but no generalization of ET to q-rung orthopair fuzzy sets (q-ROFSs) has been made yet. Therefore, this paper proposes a novel, simple, and intuitive approach to distance and similarity measures for q-ROFSs based on belief and plausibility functions within the framework of ET. This research addresses a significant research gap by introducing a comprehensive framework for handling uncertainty in q-ROFSs using ET. Furthermore, it acknowledges the limitations inherent in the current state of research, notably the absence of generalizations of ET to q-ROFSs and the challenges in extending belief and plausibility measures to certain aggregation operators and other generalizations including Hesitant fuzzy sets, Bipolar fuzzy sets, Fuzzy soft sets etc. Our contribution lies in the proposal of a novel approach to distance and similarity measures for q-ROFSs under ET, utilizing Orthopairian belief and plausibility intervals (OBPIs). We establish new similarity measures within the generalized ET framework and demonstrate the reasonability of our method through useful numerical examples. Additionally, we construct Orthopairian belief and plausibility GRA (OBP-GRA) for managing daily life complex issues, particularly in multicriteria decision-making scenarios. Numerical simulations and results confirm the usability and practical applicability of our proposed method in the framework of ET. [ABSTRACT FROM AUTHOR]

Due to the digitization of data and the dynamic requirements of the users, cloud computing is one of the most used technologies in the present scenario. Cloud computing provides a platform to store, process, and share data remotely for heterogeneous users and provides services according to the requests generated by those users. However, its rapid growth has led to one of the major challenges in the environment; the security and privacy of the data. To address the security and privacy concerns, in this paper, our major contribution is a trusted computing framework namely Secure Framework using Behavior and Role Analysis (SFBRA) for cloud data security. The framework utilizes user log monitoring data, pattern recognition algorithms, and role-based access mechanisms to detect malicious and suspicious activities of different users. Our proposed framework provides two levels of security for cloud users. In Level-1, we calculate the trust value of the logged-in users by analyzing the existing log table and pattern of request access. In Level-2, we calculate the trust of the request (storage, processing, sharing) data packet using behavior analysis of the user and a role-based access mechanism and finally detect the malicious activities. The efficacy of our proposed framework is demonstrated through experimentation, where we compare our framework with existing research works. The results show 95% accuracy in potential attack detection and prevention,approximately 8 Mbps throughput, and 0.003% packet drop on average. [ABSTRACT FROM AUTHOR]

The implementation of Deep Learning (DL) Prediction techniques for Human Age Prediction (HAP) has been widely researched and studied to prevent, treat, and extend life expectancy. While most algorithms rely on facial images, MRI scans, and DNA methylation for training and testing, they are seldom implemented due to a lack of significant validation and evaluation in real-world scenarios, low performance, and technical challenges. To address these issues, this paper proposes the Data, Classification Technique, Prediction, and View (DCPV) taxonomy, which outlines the primary components required to implement and validate a deep learning model for predicting human age. By providing a common baseline for end-users and researchers, this taxonomy offers a clearer view of the constituents of deep learning prediction approaches, enabling the development of similar systems in the health domain. In contrast to existing machine learning methods, the proposed taxonomy emphasizes the value of deep learning practices based on performance, accuracy, and efficiency in predicting human age. To validate the DCPV taxonomy, the study examines 31 state-of-the-art research journal articles within the HAP system domain, assessing the taxonomy's performance, accuracy, robustness, and model comparisons. [ABSTRACT FROM AUTHOR]

In this paper proposes an algorithm for the analytical calculation of convolutional neural networks without using neural network training algorithms. A description of the algorithm is given, on the basis of which the weights and threshold values of a convolutional neural network are analytically calculated. In this case, to calculate the parameters of the convolutional neural network, only 10 selected samples were used from the MNIST digit database, each of which is an image of one of the recognizable classes of digits from 0 to 9, and was randomly selected from the MNIST digit database. As a result of the operation of this algorithm, the number of channels of the convolutional neural network layers is also determined analytically. Based on the proposed algorithm, a software module was implemented in the Builder environment C++, on the basis of which a number of experiments were carried out with recognition of the MNIST database. The results of the experiments described in the work showed that the computation time of convolutional neural networks is very short and amounts to fractions of a second or a minute. Analytically computed convolutional neural networks were tested on the MNIST digit database, consisting of 1000 images of handwritten digits. The experimental results showed that already using only 10 selected images from the MNIST database, analytically calculated convolutional neural networks are able to recognize more than half of the images of the MNIST database, without application of neural network training algorithms. In general, the study showed that artificial neural networks, and in particular convolutional neural networks, are capable of not only being trained by learning algorithms, but also recognizing images almost instantly, without the use of learning algorithms using preliminary analytical calculation of the values of the neural network's weights. [ABSTRACT FROM AUTHOR]

Picture fuzzy sets (PFSs), as a generalization of traditional fuzzy sets and intuitionistic fuzzy sets (IFSs), offer a powerful framework for modeling and dealing with imprecise and uncertain information and have been widely used in various fields. Nevertheless, how to effectively measure the differences or similarities between PFSs is still a challenging and urgent problem. Despite that some distance measures for PFSs have been developed, some of them have counterintuitive and unreasonable results and even partially fail to satisfy the axiomatic definition of distance measures. To handle these limitations, in this paper, we propose two distance measures of PFSs motivated by the merits of Jensen–Shannon divergence. Specifically, the first distance measure of the PFSs considers the positive degree, neutral degree and negative degree. The second distance measure introduces the refusal degree on this basis. Moreover, the proposed distance measures not only satisfy the axiomatic definition but also overcome the counterintuitive results of existing distance measures. Finally, two kinds of applications related to pattern recognition and medical diagnosis are used to verify the performance of the proposed distance measures. [ABSTRACT FROM AUTHOR]

Divergence measure between intuitionistic fuzzy sets (IFSs) is important due to its wide range of applications in various fields including pattern recognition, image segmentation, decision-making and clustering. This paper introduces the characterization of belief and plausible intuitionistic fuzzy sets (BP-IFSs) to explore novel logarithmic and non-logarithmic divergence measures between two BP-IFSs. These measures are regarded as highly useful approaches to express ambiguous information within the framework of Dempster–Shafer Theory (DST). An axiomatic definition based on proposed divergence measures is also stated within a frame work of newly established theory. Furthermore, the proposed divergence measures are utilized in three different applications: (i) an example related to the recognition of BP-IFS patterns is provided to demonstrate the practicality of the proposed method in pattern recognition. (ii) An example of Hierarchical agglomerative clustering is also provided. (iii) Introduces an innovative Belief and Plausible Combinative Distance-based Assessment (BP-CODAS) method based on proposed measures for resolving Multicriteria Decision Making (MCDM) problems connected to child labor in under developed countries. The examples provided in these different directions are sufficient to demonstrate the effectiveness, applicability and viability of the suggested methods within the framework of generalized DST. [ABSTRACT FROM AUTHOR]

Automatic emotion recognition from speech signals is an important research area. Many speech emotion recognition (SER) methods have been proposed, among which ensemble learning is an effective way to recognize speech emotion. However, the ability and diversity of the base classifier are not carefully considered in the case of limited available speech emotion samples. To overcome the above problem, this paper proposes a new diversity subspace generation based on feature selection (DSGFS) for SER. In DSGFS, a constrained problem is cleverly designed, which can iteratively select many diversity and strong subspaces, so the classification ability and the diversity of the corresponding base classifiers can be ensured. As a result, more features can be extracted from the data, an ensemble classifier framework with strong base classifiers can be automatically generated, and the number of base classifiers can be smaller. The proposed models offered SER weighted average recall of 87.24%, 64.58%, 69.10%, 53.50% on the EmoDB, SAVEE, RAVDESS, CASIA datasets with speaker independent, respectively, which validate the proposed approach in terms of the performance of speech emotion recognition. [ABSTRACT FROM AUTHOR]

The use of a convolutional neural network with transfer learning is a strategy that defines high-level features, commonly explored to study patterns in medical images. These features can be analyzed via different methods in order to design hybrid models with more useful and accurate solutions for clinical practice. In this paper, a computational scheme is presented to define hybrid models through deep features by transfer learning, selection by ranking and a robust ensemble classifier with five algorithms. The obtained models were applied to classify histological images from breast, colorectal and liver tissue. The strategy developed here allows knowing important results and conditions to improve models of computer-aided diagnosis, even exploring classic CNN models. The features were defined using layers from the AlexNet and ResNet-50 architectures. The attributes were organized into subsets of the most relevant features and submitted to a k-fold cross-validation process. The best hybrid models were obtained with deep features from the ResNet-50 network, using distinct layers (activation_48_relu and avg_pool) and a maximum of 35 descriptors. These hybrid models provided 98.00% and 99.32% of accuracy values, with emphasis on histological images of breast cancer, indicating the best solution among those available in the specialized Literature. Also, these models provided more relevant results for classifying UCSB and LG datasets than regularized techniques and CNN architectures, exploring data augmentation or not. The computational scheme with detailed information regarding the main hybrid models is a relevant contribution to the community interested in the study of machine learning techniques for pattern recognition. [ABSTRACT FROM AUTHOR]

In recent years, Deep Neural Networks (DNN) have emerged as a practical method for image recognition. The raw data, which contain sensitive information, are generally exploited within the training process. However, when the training process is outsourced to a third-party organization, the raw data should be desensitized before being transferred to protect sensitive information. Although masks are widely applied to hide important sensitive information, preventing inpainting masked images is critical, which may restore the sensitive information. The corresponding models should be adjusted for the masked images to reduce the degradation of the performance for recognition or classification tasks due to the desensitization of images. In this paper, we propose a mask-based image desensitization approach while supporting recognition. This approach consists of a mask generation algorithm and a model adjustment method. We propose exploiting an interpretation algorithm to maintain critical information for the recognition task in the mask generation algorithm. In addition, we propose a feature selection masknet as the model adjustment method to improve the performance based on the masked images. Extensive experimentation results based on multiple image datasets reveal significant advantages (up to 9.34% in terms of accuracy) of our approach for image desensitization while supporting recognition. [ABSTRACT FROM AUTHOR]

The prevalence of skin illnesses is higher than that of other diseases. Fungal infection, bacteria, allergies, viruses, genetic factors, and environmental factors are among important causative factors that have continuously escalated the degree and incidence of skin diseases. Medical technology based on lasers and photonics has made it possible to identify skin illnesses considerably more rapidly and correctly. However, the cost of such a diagnosis is currently limited and prohibitively high and restricted to developed areas. The present paper develops a holistic, critical, and important skin disease prediction system that utilizes machine learning and deep learning algorithms to accurately identify up to 20 different skin diseases with a high F1 score and efficiency. Deep learning algorithms like Xception, Inception-v3, Resnet50, DenseNet121, and Inception-ResNet-v2 were employed to accurately classify diseases based on the images. The training and testing have been performed on an enlarged dataset, and classification was performed for 20 diseases. The algorithm developed was free from any inherent bias and treated all classes equally. The present model, which was trained using the Xception algorithm, is highly efficient and accurate for 20 different skin conditions, with a dataset of over 10,000 photos. The developed system was able to classify 20 different dermatological diseases with high accuracy and precision. [ABSTRACT FROM AUTHOR]

Abstract—On February 6, 2023, two devastating earthquakes struck southern central Turkey, nine hours apart. The ground shaking from these earthquakes even swept over a significant part of northwestern Syria. In this paper, we consider the locations of the epicenters of these earthquakes against the prediction of the М ≥ 6.5 earthquake epicenters in Anatolia and adjacent regions, which was made in 1973 by I.M. Gelfand, V.I. Keilis-Borok and their colleagues using the Kora-3 pattern recognition algorithm based on morphostructural zoning data. We also present the results of early detection of the periods with an increased probability of the strongest earthquakes as determined by the medium-term prediction algorithm for the M8 earthquakes. It is found that the epicenters of the February 6, 2023 earthquakes occurred in a node identified in 1973 as potentially earthquake-prone for М ≥ 6.5, and both events occurred within the spatial and temporal coverage of the warning area diagnosed by the M8 algorithm in July 2021. [ABSTRACT FROM AUTHOR]

QR codes are often placed on complex backgrounds and under unstable illumination conditions, which in fact renders their localization and decoding quite challenging. In this paper, we propose a robust system devised to ensure two main tasks: firstly, extracting QR codes from captured images regardless of their quality. Secondly, enhance the extracted QR codes textures so that they can be decoded correctly. A QR code is principally localized through three finder patterns (FPs), which are placed in its three corners, as well as a set of alignment patterns (APs). FP and AP are characterized by specific textures (alternative black and white pixel sequences), defined respectively by the following ratios: 1:1:3:1:1 and 1:1:1:1:1. The proposed system starts by segmenting the captured image using an achromatic filter. This is followed by horizontal and vertical scans, aiming at extracting the valid horizontal and vertical segments satisfying the aforementioned ratios. Afterwards, the valid segment intersection is computed to localize candidate areas containing FPs and APs. The rough extracted patterns are filtered based on Multiclass Support Vector Machine classifier combined with the histogram of Oriented Gradients. The retained patterns are then transmitted to a pattern closeness measurement algorithm to conveniently localize QR code positions. In order to evaluate the robustness and weakness of the proposed approach, a comparative study has been carried out. In this study, we compared our approach against the most relevant competitive QR code extractors, such as Zxing, Zbar, Quirc, Leadtools and Dynamsoft. Our algorithm outperforms the evoked extractors with a precision rate of 97,3% and 96% under motion blur and JPEG compression respectively. Furthermore, the execution time analysis proved that our algorithm is not computationally expensive with a maximum running time of 1092 ms at a high resolution image and high number of QR codes per image.. [ABSTRACT FROM AUTHOR]

Fungi are one of the major causes that contributed to plant diseases. There are lots of fungi species but it is estimated that only 10% have been described. There are two major approaches to identifying fungi species, morphological identification, and molecular test which need cautious clarification to make good interpretations and are time-consuming. In this paper, we propose a Machine Learning approach that involves the use of the K-Means clustering technique, and Decision Tree to highlight the observed fungi spore images taken under the microscopic view and discard background pixels to produce digital images database which later can be used for Deep Learning. [ABSTRACT FROM AUTHOR]

Although there are many researches on the similarity measure for intuitionistic fuzzy sets, some of them have some defects. To overcome these shortcomings, based on the Euclidean distance between intuitionistic fuzzy set and three special intuitionistic fuzzy sets, a new similarity measure for intuitionistic fuzzy sets is proposed in this paper. The proposed similarity measure is compared with the existing similarity measures in intuitionistic fuzzy environment by numerical examples. Moreover, we apply the proposed similarity measure to pattern recognition problems in intuitionistic fuzzy environment. The experimental results show that the proposed similarity measure for intuitionistic fuzzy sets is reasonable and effective. [ABSTRACT FROM AUTHOR]

This paper considers the use of machine learning for diagnosis of diseases that is based on the analysis of a complete gene expression profile. This distinguishes our study from other approaches that require a preliminary step of finding a limited number of relevant genes (tens or hundreds of genes). We conducted experiments with complete genetic expression profiles (20 531 genes) that we obtained after processing transcriptomes of 801 patients with known oncologic diagnoses (oncology of the lung, kidneys, breast, prostate, and colon). Using the indextron (instant learning index system) for a new purpose, i.e., for complete expression profile processing, provided diagnostic accuracy that is 99.75% in agreement with the results of histological verification. [ABSTRACT FROM AUTHOR]

Brief historical background on the establishment and activities of the Institute of Control Sciences (IPU RAS). The paper presents key findings of the Institute (obtained mainly in the 20th century) in the field of pattern recognition and of related analysis of complex data. It focuses on four areas of research including (a) the method of potential functions, (b) the theory of learning and self-learning systems, (c) the generalized portrait method and recovery of dependences based on empirical data, and (d) automatic classification methods and expert classification analysis. Relations between these areas are studied. The pioneers in the field are named (M.A. Aizerman, E.M. Braverman, L.I. Rozonoer, Ya.Z. Tsypkin, V.N. Vapnik, A.Ya. Chervonenkis, I.B. Muchnik, and A.A. Dorofeyuk among others) and brief biographical notes on the life and scientific work of these scientists are presented. The follow-ups of the results thus obtained are shown. The bibliography of publications by the Institute's researchers in leading journals of Russia on pattern recognition problems and related complex data analysis tasks is provided. [ABSTRACT FROM AUTHOR]

This paper contains information about the creation, personnel, and main areas of scientific, scientific-organizational, and educational activities, as well as the main results obtained at the scientific school of the Honored Scientist of the Russian Federation, Doctor of Technical Sciences, Professor Yu.G. Vasin. [ABSTRACT FROM AUTHOR]

This paper provides a short survey of the main direction of activity of the Federal Research Center "Computer Science and Control" of the Russian Academy of Sciences (FRC CSC RAS) in the areas concerning the topics of the journal Pattern Recognition and Image Analysis. The FRC CSC RAS was founded in December 2014 through the reorganization of the Institute of Informatics Problems of the Russian Academy of Sciences (IIP RAS) (by including the Institute for System Analysis of the Russian Academy of Sciences (ISA RAS) and the Dorodnycin Computing Center of the Russian Academy of Sciences (DCC RAS)). Section 1 reviews the activity of these institutes before their merger. Section 2 studies the work performed after 2015. [ABSTRACT FROM AUTHOR]

Measuring geotechnical and natural hazard engineering features, along with pattern recognition algorithms, allows us to categorize the stability of slopes into two main classes of interest: stable or at risk of collapse. The problem of slope stability can be further generalized to that of assessing landslide susceptibility. Many different methods have been applied to these problems, ranging from simple to complex, and often with a scarcity of available data. Simple classification methods are preferred for the sake of both parsimony and interpretability, as well as to avoid drawbacks such as overtraining. In this paper, an experimental comparison was carried out for three simple but powerful existing variants of the well-known nearest neighbor rule for classifying slope/landslide data. One of the variants enhances the representational capacity of the data using so-called feature line segments, while all three consider the concept of a territorial hypersphere per prototype feature point. Additionally, this experimental comparison is entirely reproducible, as Python implementations are provided for all the methods and the main simulation, and the experiments are performed using three publicly available datasets: two related to slope stability and one for landslide susceptibility. Results show that the three variants are very competitive and easily applicable. [ABSTRACT FROM AUTHOR]

Highlights: The types, working principles, advantages and limitations of pattern recognition methods based on chemiresistive gas sensor array are reviewed and discussed comprehensively. Outstanding and novel advancements in the application of machine learning methods for gas recognition in different important areas are compared, summarized and evaluated. The current challenges and future prospects of machine learning methods in artificial olfactory systems are discussed and justified. As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) and Deep Learning (DL) have achieved high success in many textual, auditory, medical imaging, and visual recognition patterns. Concerning the importance of ML/DL in recognizing patterns due to its high accuracy, many researchers argued for many solutions for improving pattern recognition performance using ML/DL methods. Due to the importance of the required intelligent pattern recognition of machines needed in image processing and the outstanding role of big data in generating state-of-the-art modern and classical approaches to pattern recognition, we conducted a thorough Systematic Literature Review (SLR) about DL approaches for big data pattern recognition. Therefore, we have discussed different research issues and possible paths in which the abovementioned techniques might help materialize the pattern recognition notion. Similarly, we have classified 60 of the most cutting-edge articles put forward pattern recognition issues into ten categories based on the DL/ML method used: Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Generative Adversarial Network (GAN), Autoencoder (AE), Ensemble Learning (EL), Reinforcement Learning (RL), Random Forest (RF), Multilayer Perception (MLP), Long-Short Term Memory (LSTM), and hybrid methods. SLR method has been used to investigate each one in terms of influential properties such as the main idea, advantages, disadvantages, strategies, simulation environment, datasets, and security issues. The results indicate most of the articles were published in 2021. Moreover, some important parameters such as accuracy, adaptability, fault tolerance, security, scalability, and flexibility were involved in these investigations. [ABSTRACT FROM AUTHOR]

Surface roughness is a major factor controlling the shear strength of discontinuities. Various methods have so far been presented for determining surface roughness based on 2D profiles. These methods use statistical, fractal, geostatistical, directional, and spectral features of the surface profiles most of which were established through the relationship between the features and Barton standard profiles. To overcome some of the shortcomings of the standard JRC profiles, this paper presents a new method based on the investigation of 2D profiles within “pattern recognition” framework for rock surface recognition. In this way, more than 9000 profiles were gathered from 84 natural rock samples for the calculation of the features, and the joint roughness coefficient was back-calculated from test results. A representative feature vector and profile of a surface were defined, and two methods of principal component analysis (PCA) and liner discriminant analysis (LDA) were applied in order to prepare inputs for the rock surface classification. Then, minimum mean distance (MMD) and K-nearest neighbors (KNN) were used for the classification. The results show that the latter provides lower classification error (44.4% when a level difference between the predicted and back-calculated classes is accepted) due to reducing the effect of dissimilar samples. Comparison of the results with those of single-variable equations that are based on statistical (δ and Z 2 ) and directional parameters θ max ∗ 1 + C with 53.57%, 52.38%, and 48.8% errors, respectively, proves that the proposed procedure is an efficient tool for the estimation of JRC. Furthermore, based on the results obtained from the pattern recognition analysis, 10 natural and artificial reference profiles attained based on a fully quantified approach were proposed as a replacement for the current JRC profiles. [ABSTRACT FROM AUTHOR]

The publication presents an introductory paper to the Special issue of the international journal Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications of the Russian Academy of Sciences. The main scientific results of the 8th International Workshop "Image Mining: Theory and Applications," held on August 21, 2022, Montreal, Canada, are presented. Historical information is given on this series of international workshops, and their significant role in the development of the theory and practice of automation of image analysis, pattern recognition, and artificial intelligence is emphasized. The list of papers of the Special issue of PRIA, prepared based on the invited and regular papers selected and recommended for publication by the Program Committee of the IMTA-VIII-2022, is presented. [ABSTRACT FROM AUTHOR]

Sign language is a non-verbal communication tool used by the deaf. A robust sign language recognition framework is needed to develop Human–Robot Interaction (HRI) platforms that are able to interact with humans via sign language. Iranian sign language (ISL) is composed of both static postures and dynamic gestures of the hand and fingers. In this paper, we present a robust framework using a Deep Neural Network (DNN) to recognize dynamic ISL gestures captured by motion capture gloves in Real-Time. To this end, first, a dataset of fifteen ISL classes was collected in time series; then, this dataset was virtually augmented and pre-processed using the "state-image" method to produce a unique collection of images, each image corresponding to a specific set of sequential data representing a class. Next, by implementing a continuous Genetic algorithm, an optimal deep neural network with the minimum number of weights (trainable parameters) and the maximum overall accuracy was found. Finally, the dataset was fed to the DNN to train the model. The results showed that the optimization process was successful at finding a DNN structure highly suitable for this application, with 99.7% accuracy on the verification (test) data. Then, after implementing the module in a robotic architecture, an HRI experiment was conducted to assess the system's performance in real-time applications. Preliminary statistical analysis on the standard UTAUT model for eight participants showed that the system can recognize ISL signs quickly and accurately during human–robot interaction. The proposed methodology can be used for other sign languages as no specific characteristics of ISL were used in the preprocessing or training stage. [ABSTRACT FROM AUTHOR]

This paper proposes a gait intention detection (GID) method based on motion information from a single leg. This method detects three gait states: stair ascending (SA), stair descending (SD), and level walking (LW). The GID is a highly important factor for the control of gait assistive devices. If the GID is limited to the motion information from a single leg, analyzing the gait motions becomes more challenging, owing to the absence of information regarding the phase difference between both legs. To address this challenging, previous GID methods have used fusion data from several sensors. In this paper, only three inertial measurement units are used for proposed single leg GID method. To analyze gait motions using these limited sensors, the divergent component of motion (DCM) of the user is computed. The DCM is a physical quantity incorporating the linear position and linear velocity of the center of mass; these can be computed from the kinematic data of a support leg. In each of the three gait states SA, SD, and LW, the DCM shows different patterns. To classify and detect these different patterns, a pattern recognition method based on an artificial neural network algorithm is used. The GID performance of the proposed method was experimentally evaluated, and it showed 99% success rate for the SA, SD, and LW states. [ABSTRACT FROM AUTHOR]

With the development of the big data era, the manufacturing industry is focusing on integrating the technologies related to the evolving intelligent industry. Intelligent manufacturing is the latest form of advanced manufacturing development. In order to pay attention to the design and evaluation status of manufacturing system, we understand the connotation of manufacturing system from the perspective of actual manufacturing production and sales. In this paper, the evaluation model and algorithm of Intelligent Manufacturing System Based on pattern recognition and big data are proposed. In the process of studying the information transmission of intelligent manufacturing system, the evaluation hierarchy algorithm of intelligent control system is improved with reference to several modeling methods. Finally, an intelligent manufacturing system suitable for processing big data is designed. In the experiment in workshop A, with the help of computer simulation of artificial intelligent activities, the intelligent manufacturing system analyzes the processing and production data, obtains the dynamic requirements of products in the machining workshop, and provides judgment basis for the construction of dynamic units in enterprises. The experimental results show that after the change of the production organization model using the seasonal change forecasting method, the introduction of the smart manufacturing system, the sales production of this shop has been increasing, and in the 20th month has increased seven times more than the initial, while the overall situation of the predicted sales and actual sales and costs, the superiority of using the smart manufacturing evaluation model is derived. [ABSTRACT FROM AUTHOR]

Keyword Spotting (KWS) is here considered as a basic technology for Probabilistic Indexing (PrIx) of large collections of handwritten text images to allow fast textual access to the contents of these collections. Under this perspective, a probabilistic framework for lexicon-based KWS in text images is presented. The presentation aims at providing formal insights which help understanding classical statements of KWS (from which PrIx borrows fundamental concepts), as well as the relative challenges entailed by these statements. The development of the proposed framework makes it clear that word recognition or classification implicitly or explicitly underlies any formulation of KWS. Moreover, it suggests that the same statistical models and training methods successfully used for handwriting text recognition can advantageously be used also for PrIx, even though PrIx does not generally require or rely on any kind of previously produced image transcripts. Experiments carried out using these approaches support the consistency and the general interest of the proposed framework. Results on three datasets traditionally used for KWS benchmarking are significantly better than those previously published for these datasets. In addition, good results are also reported on two new, larger handwritten text image datasets (Bentham and Plantas), showing the great potential of the methods proposed in this paper for indexing and textual search in large collections of untranscribed handwritten documents. Specifically, we achieved the following Average Precision values: IAMDB: 0.89, George Washington: 0.91, Parzival: 0.95, Bentham: 0.91 and Plantas: 0.92. [ABSTRACT FROM AUTHOR]

Printed fabrics often have rich colors and variable patterns in different sizes and shapes, which make it difficult to achieve accurate pattern recognition and color measurement using traditional spectrophotometers and digital cameras. This paper develops a grid-based density peaks clustering (GDPC) algorithm to automatically recognize patterns and extract colors of multispectral images of printed fabrics. The multispectral images captured by a self-developed multispectral imaging system is firstly converted into color images in CIELAB color space and three principal components are calculated by applying principal component analysis to reduce the dimensions of the multispectral images. During the multispectral image processing, the noise pixels are removed by calculating the local stability of each pixel, and then the remaining stable pixels are clustered using proposed GDPC algorithm based on three CIELAB color channels and three principal components. Compared with widely-used color clustering algorithms, the proposed GDPC algorithm can recognize the color patterns from more intricate multispectral printed fabric images with higher accuracy and less computational time. [ABSTRACT FROM AUTHOR]

The mechanism of L2-norm loss can be explained from the perspective of maximizing margin and minimizing margin variance, which is equivalent to the idea of margin distribution. Recent studies suggest that the margin distribution is more vital for the generalization performance of models. Inspired by the idea, a nonparallel support vector machine (NPSVM) with L2-norm loss is proposed in this paper, termed as L2-NPSVM. We first reconstruct a L2-norm loss for NPSVM. The L2-norm loss can make full use of the margin distribution information in the training samples. Based on the L2-norm loss, we formulate the L2-NPSVM algorithm. L2-NPSVM is a more reliable training model with stronger generalization ability. Since L2-norm loss is used to measure the empirical risk in the objective function, L2-NPSVM can also reduce the impact of outliers. Furthermore, due to the L2-norm loss, the dual coordinate descent (DCD) method can be applied to linear and nonlinear L2-NPSVM. Under the premise of high accuracy, DCD can accelerate the solving process. At last, we conduct a series of comparative experiments on UCI datasets, noise-added UCI datasets and real-world datasets, which demonstrate the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Similarity measures are very effective and meaningful tool used for evaluating the closeness between any two attributes which are very important and valuable to manage awkward and complex information in real-life problems. Therefore, for better handing of fuzzy information in real life, Ullah et al. (Complex Intell Syst 6(1): 15–27, 2020) recently introduced the concept of complex Pythagorean fuzzy set (CPyFS) and also described valuable and dominant measures, called various types of distance measures (DisMs) based on CPyFSs. The theory of CPyFS is the essential modification of Pythagorean fuzzy set to handle awkward and complicated in real-life problems. Keeping the advantages of the CPyFS, in this paper, we first construct an example to illustrate that a DisM proposed by Ullah et al. does not satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Then, combining the 3D Hamming distance with the Hausdorff distance, we propose a new DisM for CPyFSs, which is proved to satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Moreover, similarly to some DisMs for intuitionistic fuzzy sets, we present some other new complex Pythagorean fuzzy DisMs. Finally, we apply our proposed DisMs to a building material recognition problem and a medical diagnosis problem to illustrate the effectiveness of our DisMs. Finally, we aim to compare the proposed work with some existing measures is to enhance the worth of the derived measures. [ABSTRACT FROM AUTHOR]

This paper presents an approach for identifying fingerprints through the extraction of geometric and statistical features of characteristic Minutiae. The proposed approach is in accordance with statistical features to extract important points from the skeleton of a fingerprint's image. Through the addition of geometric features as a kind of preprocessing to this approach, the images are divided into distinct regions. In this approach, statistical parameters like min, max, mean and standard deviation are applied in order to compute the general abstract of the features. Another achievement in this article is the presentation of a similarity measure in identification tools which is only used for methods based on matching patterns. The Optimized version of the proposed method achieved near zero EER percentage in some of the datasets. [ABSTRACT FROM AUTHOR]

Broad neural networks provide an alternative way of deep learning with a novel flatted structure, which employ a non-iterative training mechanism and exhibit high efficiency in various recognition tasks. However, such broad networks unavoidably suffer from unstable performance due to the double random mappings in the generation of feature representations and the consequent uncertainty. The existing research often neglect to explore the effect of the quality of the broaden feature representations, which is crucial for the model performance. This paper presents a progressively-enhanced framework taking broad network as basic learners (PE-BL) to address the existing issues. The basic broad learners in PE-BL are trained in sequence, and the core manipulation is to modify the primitive hidden feature representations of the current learner through the nonlinear transformation of the prediction from the previous one, so the resulting broaden representations become more discriminative. Further, PE-BL is adapted to the scenarios where only a single broad learner is employed. Finally, extensive comparative experiments on some benchmark datasets and Electroencephalogram (EEG)-based emotion recognition task verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

A speaker identification framework that combines both high- and low-level features, for state-of-the-art variability analysis and classification is proposed in this paper. The proposed framework introduces a workable solution that targets robust speaker variability classification using speech samples recorded in suboptimal conditions. A translated Ibibio (New Benue-Congo, Nigeria) version of "The Tiger and the Mouse"—a prosodically balanced corpus that demonstrates the prosody of read-aloud English was used in this study with speech samples obtained from 50 participants (25 males and 25 females). Identity-vectors (i-vectors) or low dimensional signal patterns were extracted and used as baselines for investigating speakers' variability patterns across various classifiers (Decision Tree: DT, Support Vector Machine: SVM, k-Nearest Neighbour: k-NN, and Deep Neural Network: DNN) and kernels. Treatment of the baselines with high level features (speech duration, F0, intensity) was also experimented for word, syllable, and phoneme units. Results obtained revealed that DTs and some variants of SVM gave high classification accuracies (above 70%). Hence, the hypothesis of universal Gaussianity appears inexact, as the linear predictor that is optimal in the mean square error sense may not hold for Ibibio. Further treatments of the baselines with Linear Discriminant Analysis (LDA) and Cosine Distant Scoring (CDS) yielded very poor classification results, except for the k-NN and Gaussian SVM classifiers which performed well for the LDA treated baselines. [ABSTRACT FROM AUTHOR]

In this paper, we present a new approach for defining a dual proximity measure for intuitionistic fuzzy sets. The new approach utilizes an extended form of the restricted equivalence functions and receives their values as a two tuple. The two values in the two tuple are due to the dual character of the proximity measure under consideration. In fact, the computation of proximity between two intuitionistic fuzzy sets simultaneously provides the value of similarity as well as non-similarity between the intuitionistic fuzzy sets. So, the novel approach proposed in this paper provides a comprehensive information theoretic evaluation of intuitionistic fuzzy sets. Further, we investigate the application of the novel measure in pattern recognition and clustering analysis. We also contrast the performance of the proposed measure with the state-of-art in view of the structured linguistic variables, pattern classification and clustering analysis. The comparative analysis shows that the newly introduced two-valued proximity measure performs better in certain circumstances and encompasses the dualism of the human cognition. [ABSTRACT FROM AUTHOR]

The paper considers and analyzes the main scientific results of the 15th International Conference on Pattern Recognition and Information Processing (PRIP-2021), Minsk, Republic of Belarus, September 2021. The history of this series of conferences is outlined and its significant role in the development of the theory and practice of image analysis, pattern recognition, and artificial intelligence is indicated. A list of articles in the special issue prepared on the basis of reports selected by the PRIP-2021 program committee is provided. [ABSTRACT FROM AUTHOR]

In this paper, two new distance measures are introduced, which are used in various application problems in decision-making, pattern recognition, and clustering. Intuitionistic fuzzy sets are sources of information that contain both the membership and non-membership degrees of the elements in the set. As such, distance measures based on geometric concepts are sometimes misleading. Hence, six parameters are identified to construct the distance measures. These parameters are membership information dissimilarity, non-membership information dissimilarity, hesitancy information dissimilarity, product cross-information dissimilarity, maximum cross-information dissimilarity, and minimum cross-information dissimilarity. Among all the parameters, the product cross-information dissimilarity is newly introduced in this work. Compensations for the proposed distance measures are established by various counter-intuitive problems in decision-making and pattern recognition. Finally, validation of the proposed distance measures is established by diverse problems of applications in decision-making, pattern recognition, and clustering problems. [ABSTRACT FROM AUTHOR]

In the field of artificial intelligence, pattern recognition is widely used to extract the abstract information in those high dimensional inputs of image, voice, or video. However, the interpretability of pattern recognition still remains understudied. The incomplete features extracted from system input still limit the recognition performance. To reject the disturbance of feature incompleteness, an error compensation is realized into the pattern recognition model under a quantum computation framework. The quantum-based recognition system fulfills the information transmission from input to output with the transformation of quantum states. Then, a compensation for the quantum state is used to reject those intermediate errors in the pattern recognition task. The experiment results in this paper indicate an effectiveness of the proposed method, with which the compensated Quantum Neural Network obtains a better performance. The proposed method brings a more robust recognition system under unknown disturbances. [ABSTRACT FROM AUTHOR]

Plankton are an important component of life on Earth. Since the 19th century, scientists have attempted to quantify species distributions using many techniques, such as direct counting, sizing, and classification with microscopes. Since then, extraordinary work has been performed regarding the development of plankton imaging systems, producing a massive backlog of images that await classification. Automatic image processing and classification approaches are opening new avenues for avoiding time-consuming manual procedures. While some algorithms have been adapted from many other applications for use with plankton, other exciting techniques have been developed exclusively for this issue. Achieving higher accuracy than that of human taxonomists is not yet possible, but an expeditious analysis is essential for discovering the world beyond plankton. Recent studies have shown the imminent development of real-time, in situ plankton image classification systems, which have only been slowed down by the complex implementations of algorithms on low-power processing hardware. This article compiles the techniques that have been proposed for classifying marine plankton, focusing on automatic methods that utilize image processing, from the beginnings of this field to the present day. [ABSTRACT FROM AUTHOR]

Dissimilarity measure on intuitionistic fuzzy sets (IFS) are often used in various problems of decision-making, pattern recognition and clustering problems. Numerous dissimilarity measures are constructed based on diverse mathematical concepts. However, not all dissimilarity measure might be suitable for a specific problem or definition. Hence, one should be specific about the nature of the decision to be drawn and choose an appropriate dissimilarity measure. This article aims to classify the dissimilarity measure as moderate, pessimistic and optimistic based on the information held by the IFS. The majority of the currently available dissimilarity measures take into account the moderate viewpoint of information in the IFS. There are a few dissimilarity measures that account for the pessimistic viewpoint of information in the IFSs. However, in some cases, the optimistic viewpoint of the information in the IFSs is also required. In addition, the cross-information factor is an important element of a dissimilarity measure. Consequently, a new dissimilarity measure is being developed that is optimistic and also takes the cross-information factor into account by calculating the change between the maximum and minimum cross-information factors. Furthermore, the dissimilarity measure is designed in such a way that it satisfies the dissimilarity properties under the complete containment relation. Moreover, a method for generating induced dissimilarity measures from a given dissimilarity measure using monotonic functions has been proposed. Numerical and comparative studies demonstrate the out-performance and superiority of the proposed dissimilarity measure, as well as highlights the unique characteristic of an optimistic nature. Finally, the proposed dissimilarity measure is being used to solve variety of application problems, including decision-making, pattern recognition, and clustering. [ABSTRACT FROM AUTHOR]

High accuracy and fast computation time are essential in implementing hand gesture pattern recognition for prosthetic hand using electromyography (EMG) signal. However, several physical parameters affect the characteristics of the EMG signal, including forearm orientation. Therefore, this study aims to develop a deep learning classifier using convolution neural network (CNN) algorithm that maintains accuracy with forearm orientation changes. The main advantage of this method is the simplicity (without feature extraction process) and able to maintain the accuracy against the orientation changes. This method consists of a two-dimensional convolution, max-pooling, four fully connected and output layer. The input layer classifier received six channels of raw EMG signal derived from ten able bodies. As a comparison, several conventional classifiers including support vector machine, k-nearest neighborhood, linear discriminant analysis and decision tree were applied to examine the performance among the classifiers. The result showed that the accuracy of the proposed CNN classifier based on all orientation outperfomed other classifers (96.8 ± 1.87%). Furthermore, the difference in accuracy among the orientations was less then 5%. This indicates that the classifier is able to maintain high accuracy with changes in orientation. In conclusion, this study is applicable in the development of prosthetic hands using EMG signal as control with constant accuracy when the forearm orientation varies. [ABSTRACT FROM AUTHOR]

Obtaining labeled images and samples is a very expensive process and can require intensive labor. At the same time, there are often not enough labeled samples to train an effective classifier. Graph-based semi-supervised methods have attracted much attention in the field because they can use both labeled and unlabeled data. In this letter, we suggest a novel graph-based semi-supervised learning approach that takes full advantage of a small set of labeled samples and a large set of unlabeled samples. We first explain the concept of graph-based semi-supervised learning. Our central proposal is to jointly estimate a low-rank graph together with a latent subspace and soft labels. The proposed system exploits the synergy between the graph information and the latent data representation. This extends and enriches the supervision information in the semi-supervised context and produces a good discriminative linear mapping between the original space and the latent subspace. Several experiments were performed with five image datasets using state-of-the-art methods. The results of the study reveal several noteworthy findings. The proposed framework generally outperforms recent competing approaches in estimating labels and linear embeddings, especially for large datasets such as MNIST. The superiority is maintained even for minimally labeled images, but the recognition accuracy does not always increase. However, in a few cases, the proposed method may be outperformed by other method when the number of labeled samples per class is only one (the dataset UMIST). When we set the number of labeled samples per class to 2, we find that the proposed method consistently performs better than the competing methods. For the extended Yale dataset, the accuracy of the proposed method is 67.08%. This is 1.68% higher than the accuracy of the nearest competing method and an impressive 30.08% higher than the accuracy of the farthest competing method. In the case of the FacePix dataset, the accuracy of the proposed method is 58.08%. This is 2.58% higher than the nearest competing method and 13.58% higher than the farthest competing method. For the UMIST dataset, the accuracy reaches 54.2%, outperforming the nearest competing method by 0.9% and the farthest competing method by 7.5%. We have introduced a novel graph-based semi-supervised method capable of jointly predicting the soft labels and linear embedding and constructing a data graph. The main contribution in this work is the integration of the graph estimation into the objective function of the model. By computing the graph structure while estimating the semi-supervised model, a more optimal solution can be obtained. The synergistic use of data features and adaptive soft labels has indeed contributed to the estimation of a good discriminant model. [ABSTRACT FROM AUTHOR]

As one of the most important assets in the transportation of oil and gas products, subsea pipelines are susceptible to various environmental hazards, such as mechanical damage and corrosion, that can compromise their structural integrity and cause catastrophic environmental and financial damage. Autonomous underwater systems (AUS) are expected to assist offshore operations personnel and contribute to subsea pipeline inspection, maintenance, and damage detection tasks. Despite the promise of increased safety, AUS technology needs to mature, especially for image-based inspections with computer vision methods that analyze incoming images and detect potential pipeline damage through anomaly detection. Recent research addresses some of the most significant computer vision challenges for subsea environments, including visibility, color, and shape reconstruction. However, despite the high quality of subsea images, the lack of training data for reliable image analysis and the difficulty of incorporating risk-based knowledge into existing approaches continue to be significant obstacles. In this paper, we analyze industry-provided images of subsea pipelines and propose a methodology to address the challenges faced by popular computer vision methods. We focus on the difficulty posed by a lack of training data and the opportunities of creating synthetic data using risk analysis insights. We gather information on subsea pipeline anomalies, evaluate the general computer vision approaches, and generate synthetic data to compensate for the challenges that result from lacking training data, and evidence of pipeline damage in data, thereby increasing the likelihood of a more reliable AUS subsea pipeline inspection for damage detection. [ABSTRACT FROM AUTHOR]

Discovering patterns and detecting anomalies in individual travel behavior is a crucial problem in both research and practice. In this paper, we address this problem by building a probabilistic framework to model individual spatiotemporal travel behavior data (e.g., trip records and trajectory data). We develop a two-dimensional latent Dirichlet allocation (LDA) model to characterize the generative mechanism of spatiotemporal trip records of each traveler. This model introduces two separate factor matrices for the spatial dimension and the temporal dimension, respectively, and use a two-dimensional core structure at the individual level to effectively model the joint interactions and complex dependencies. This model can efficiently summarize travel behavior patterns on both spatial and temporal dimensions from very sparse trip sequences in an unsupervised way. In this way, complex travel behavior can be modeled as a mixture of representative and interpretable spatiotemporal patterns. By applying the trained model on future/unseen spatiotemporal records of a traveler, we can detect her behavior anomalies by scoring those observations using perplexity. We demonstrate the effectiveness of the proposed modeling framework on a real-world license plate recognition (LPR) data set. The results confirm the advantage of statistical learning methods in modeling sparse individual travel behavior data. This type of pattern discovery and anomaly detection applications can provide useful insights for traffic monitoring, law enforcement, and individual travel behavior profiling. [ABSTRACT FROM AUTHOR]

Delamination between layers of composite greatly reduces their strength and stiffness, and thus, it is necessary to investigate the heat treatment method of 3D printed continuous fiber-reinforced composites to enhance their interlaminar properties. In this paper, 3D printed composites with different fiber volume fractions were treated at high temperature, and the damage behavior and failure mechanism were analyzed by acoustic emission technology. The dominant frequency of a single waveform was obtained by extracting energy features through wavelet packet transform, and the dominant damage mode of each acoustics emission (AE) event was analyzed and accurately classified. Then, support vector machine algorithm is performed to achieve supervised classification of AE signals. The result shows the maximum load of Specimen A-4 (5% fiber) and B-4 (15% fiber) increased by 83.58% and 86.8%, respectively, after being treated at 180 °C. It can be known that the temperature post-treatment makes the composite have a stronger ability to resist deformation. The temperature treatment will improve the mechanical properties of the composites but simultaneously generate more high-frequency AE signals. [ABSTRACT FROM AUTHOR]

With the vigorous development of the Internet, multimedia technology, and virtual reality technology, digital museum has been born and gradually entered the stage of rapid development. As a typical application of 3D virtual scene in the field of traditional culture protection and communication, 3D virtual museum has become an important trend in the development of museums in recent years. Three-dimensional virtual museum can maximize the development of museum functions and better play the museum's cultural communication and heritage protection and other functions. Faced with such rich cultural relic resources, how to construct 3D virtual museum quickly becomes an important research problem. Virtual reality technology integrates the latest research and development achievements of digital image graphics processing technology, computer technology, artificial intelligence, multimedia technology, sensor technology, and other information technologies, which provides powerful support and help for us to create and experience virtual world. The combination of virtual reality technology and cultural heritage protection has created a new research field. Based on this, this paper studies personalized healthcare museum exhibition system design based on the VR and deep learning-driven multimedia and multimodal sensing. The novel computational model is designed to improve the systematic performance of the VR framework, and the application scenarios are tested. Through the verification, the performance is tested. [ABSTRACT FROM AUTHOR]

Since 2019, the Russian–Italian experiment "UV Atmosphere" (Mini-EUSO) has been operational on the International Space Station. The primary instrument of this experiment is a wide-angle telescope positioned toward nadir. Its main objective is to generate an ultraviolet map of the Earth's nocturnal atmosphere radiation. This map serves as a crucial element in the preparation of a large-scale experiment involving the study of extremely high-energy cosmic rays using an orbiting telescope. Similar to the preceding TUS experiment, the "UV Atmosphere" instrument detects signals from various atmospheric processes in the ultraviolet range, including the luminosity of meteors. In this paper, we describe two simple neural networks that effectively extract meteor signals from the overall data stream. The proposed approach can also be applied to identify track-like signals of various origins in the data obtained from fluorescent and Cherenkov telescopes. [ABSTRACT FROM AUTHOR]

In the field of pattern recognition, collected data always include some additional information which are usually termed as privileged information. The privileged information is latent information belonging to the training samples, which can be easily ignored. The privileged information can help to build a better classifier for classification. In this paper, we try to construct a robust nonparallel support vector machine (NPSVM) model under the privileged information learning (LUPI) setting, termed as R-NPSVM+. On the one hand, we introduce the privileged information into NPSVM so as to build a model for classification. In the training process, both the privileged information and "usual" samples are used to train the model, which can enhance the accuracy. On the other hand, due to the ε-insensitive loss and hinge loss, NPSVM is sensitive to noise or outliers. Hence, we use two robust loss functions in R-NPSVM+ model, which can further ensure the robustness of the model. In addition, we use the Lagrange multiplier method and the dual coordinate descent (DCD) algorithm to optimize the proposed objective function, respectively. Lastly, to evaluate the performance of R-NPSVM+, we conduct a series of experiments. Experimental results confirm that compared with other classical SVM-type algorithms, our R-NPSVM+ can produce a better performance, especially when the samples are corrupted by noise and outliers. [ABSTRACT FROM AUTHOR]