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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]

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]

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]

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]

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]

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]

Understanding the glassy nature of neural networks is pivotal both for theoretical and computational advances in Machine Learning and Theoretical Artificial Intelligence. Keeping the focus on dense associative Hebbian neural networks (i.e. Hopfield networks with polynomial interactions of even degree P > 2 ), the purpose of this paper is twofold: at first we develop rigorous mathematical approaches to address properly a statistical mechanical picture of the phenomenon of replica symmetry breaking (RSB) in these networks, then—deepening results stemmed via these routes—we aim to inspect the glassiness that they hide. In particular, regarding the methodology, we provide two techniques: the former (closer to mathematical physics in spirit) is an adaptation of the transport PDE to this case, while the latter (more probabilistic in its nature) is an extension of Guerra’s interpolation breakthrough. Beyond coherence among the results, either in replica symmetric and in the one-step replica symmetry breaking level of description, we prove the Gardner’s picture (heuristically achieved through the replica trick) and we identify the maximal storage capacity by a ground-state analysis in the Baldi-Venkatesh high-storage regime. In the second part of the paper we investigate the glassy structure of these networks: at difference with the replica symmetric scenario (RS), RSB actually stabilizes the spin-glass phase. We report huge differences w.r.t. the standard pairwise Hopfield limit: in particular, it is known that it is possible to express the free energy of the Hopfield neural network (and, in a cascade fashion, all its properties) as a linear combination of the free energies of a hard spin glass (i.e. the Sherrington–Kirkpatrick model) and a soft spin glass (the Gaussian or ”spherical” model). While this continues to hold also in the first step of RSB for the Hopfield model, this is no longer true when interactions are more than pairwise (whatever the level of description, RS or RSB). For dense networks solely the free energy of the hard spin glass survives. As the Sherrington–Kirkpatrick spin glass is full-RSB (i.e. Parisi theory holds for that model), while the Gaussian spin-glass is replica symmetric, these different representation theorems prove a huge diversity in the underlying glassiness of associative neural networks. [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]

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]

In order to effectively identify the pattern of personalized adaptive learning in online education and improve the recommendation satisfaction of personalized learning resources in online education platform, this paper studies the pattern recognition method of personalized adaptive learning in online education. The learning behavior pattern data in the online education platform are mined, preprocessed, clustered and made correlation analysis, and the obtained data are used to construct the learner's personalized adaptive learning characteristics model; on this basis, the framework of learning pattern recognition model is constructed to recognize the personalized adaptive learning pattern from four aspects: cognitive level, learning style, interactive behavior pattern characteristics and online social learning characteristics. The experimental test results show that this method can effectively identify the personalized adaptive learning patterns of learners, including interactive learning behavior patterns and online social learning patterns. The personalized learning resources recommended by the online education platform according to the identification results of this paper have obtained the learners' satisfaction score of a high level at 93.27%. [ABSTRACT FROM AUTHOR]

Tool wear is inevitable in cutting process. If tool wear failure is not detected in time, it will lead to abnormal cutting process and affect the machining efficiency and quality seriously. The intelligent monitoring of tool wear can make the machining system perceive the real-time status of tools in advance and make early warning and decision-making, which is an effective way to ensure the efficient operation of machining and manufacturing system. By reviewing the research status of intelligent monitoring of tool wear, the key technical principles and methods of multisource-correlation signal selection, feature extraction and pattern recognition are classified. On the basis, the current application status of tool wear monitoring is discussed. In view of its shortcomings, this paper puts forward the prospect of the future, in order to provide a theoretical basis and reference for the development of tool wear intelligent monitoring technology and intelligent manufacturing industry. [ABSTRACT FROM AUTHOR]

Acquisition, classification, and analysis of seismic data are crucial tasks in volcano monitoring. The large number of seismic signals that are continuously acquired during the first monitoring stage poses a huge challenge for the human experts that must classify and analyze them. Several automatic classification systems have been proposed in the literature to alleviate such an overwhelming workload, each one characterized by different levels of accuracy, computational complexity, and interpretability. Considering this last perspective, which represents one of the recent key issues in geoscience, it is possible to find many accurate methods (in terms of classification accuracy) which however represent black boxes, not permitting a clear interpretation. On the other hand, there are other approaches, such as those based on support vector machines (SVM), random forests (RF), and K-nearest neighbor (KNN), which permit the interpretation of results, rules, and models at different levels. Among these last techniques, KNN approaches for volcanic signal classification typically do not achieve the satisfactory classification results obtained with RF and SVM. One possible reason is that in this context, the KNN rule has usually been applied in its basic version, not exploiting the different advanced KNN variants that have been introduced in recent years. This paper takes one step along this direction, investigating the suitability of a number of advanced versions of the KNN rule for the problem of classifying seismic-volcanic signals. The usefulness of these rules, in comparison with the original KNN rule as well as other interpretable classifiers, is evaluated within a real-world scenario involving a five-class dataset of seismic signals acquired at the Nevado del Ruiz volcano, Colombia. The results show that the classification accuracy of basic KNN is largely improved by these advanced variants, even surpassing that obtained with other classifiers like RF and SVM. [ABSTRACT FROM AUTHOR]

Systematic pattern recognition as well as the corresponding description of determined patterns entail numerous challenges in the application context of high-dimensional communication data. These can cause increased effort, especially with regard to machine-based processing concerning the determination of regularities in underlying datasets. Due to the increased expansion of dimensions in multidimensional data spaces, determined patterns are no longer interpretable by humans. Taking these challenges into account, this paper investigates to what extent pre-defined communication patterns can be interpreted for the application area of high-dimensional business communication data. An analytical perspective is considered by taking into account a holistic research approach and by subsequently applying selected Machine Learning methods from Association Rule Discovery, Topic Modelling and Decision Trees with regard to the overall goal of semi-automated pattern labelling. The results show that meaningful descriptions can be derived for the interpretation of pre-defined patterns. [ABSTRACT FROM AUTHOR]

Among the most classic measures in single valued neutrosophic sets (SVNS) theory, the distance is an important tool to compare and calculate degree of difference between SVNS. Although there exist some types of distance for single valued neutrosophic sets, most of them lack of strictly axiomatic definition and exist counter-intuitive cases. In this paper, a novel distance between single valued neutrosophic sets based on matrix norm is given. Then we proved that the new distance satisfies the axiomatic definition of the metric. Finally, the distance is applicated to pattern recognition and medical diagnoses. [ABSTRACT FROM AUTHOR]

Raman spectroscopy can characterize the rich molecular vibration information of soybean milk powder samples, but difficulties arise in its direct use for sample classification and identification. Therefore, it is urgent to develop an intelligent identification technology based on Raman spectroscopy. For brand identification of soybean milk powder, this work investigates and discusses a variety of spectral processing technologies including wavelet denoising, normalization, principal component analysis, and the results show that appropriate spectral processing can improve the recognition accuracy of the random forest algorithm. Under the optimal conditions (db2 wavelet, normalization, principal component analysis, 30 decision trees), the best recognition effect of soybean milk brand identification can be achieved. The Raman spectral signal acquisition time of each sample is 40 s, and the spectra pretreatment and identification operation time only takes a few minutes. The analytical approach established in this paper has the advantages of convenient and fast Raman spectra acquisition, fast and accurate model identification. [ABSTRACT FROM AUTHOR]

Nowadays, political parties have widely adopted social media for their party promotions and election campaigns. During the election, Twitter and other social media platforms are used for political coverage to promote the party and its candidates. This research discusses and estimates the stability of many volumetric social media approaches to forecast election results from social media activities. Numerous machine learning approaches are applied to opinions shared on social media for predicting election results. This paper presents a machine learning model based on sentiment analysis to predict Pakistan's general election results. In a general election, voters vote for their favorite party or candidate based on their personal interests. Social media has been extensively used for the campaign in Pakistan general election 2018. Using a machine learning technique, we provide a five-step process to analyze the overall election results, whether fair or unfair. The work is concluded with detailed experimental results along with discussion on the outcomes of sentiment analysis for real-world forecasting and approval of general elections in Pakistan. [ABSTRACT FROM AUTHOR]

The orthogonal moments have recently achieved outstanding predictive performance and become an indispensable tool in a wide range of imaging and pattern recognition applications, including image reconstruction, image classification and object detection. We present in this paper, a new set of orthogonal functions, called "Orthogonal helmet functions." Using these functions we introduce three new sets of orthogonal moments and their invariants to scaling, rotation and translation for image representation and recognition, named, respectively, "the orthogonal helmet-Fourier moments" for the gray-level images, the multi-channel orthogonal helmet-Fourier moments and the quaternion orthogonal helmet-Fourier moments (QHFMs) for the color images. We introduce a series of experimental tests in image analysis and pattern recognition to validate the theoretical framework of our approach. The performance of these feature vectors is compared with the existing orthogonal invariant moments. The results of the comparative study show the efficiency and the superiority of our three orthogonal invariant moments. Thanks to our orthogonal moments QHFMs, we were able to lift the image recognition quality with rate can reach 2.06 %. [ABSTRACT FROM AUTHOR]

The systematic processing of unstructured communication data as well as the milestone of pattern recognition in order to determine communication groups in negotiations bears many challenges in Machine Learning. In particular, the so-called curse of dimensionality makes the pattern recognition process demanding and requires further research in the negotiation environment. In this paper, various selected renowned clustering approaches are evaluated with regard to their pattern recognition potential based on high-dimensional negotiation communication data. A research approach is presented to evaluate the application potential of selected methods via a holistic framework including three main evaluation milestones: the determination of optimal number of clusters, the main clustering application, and the performance evaluation. Hence, quantified Term Document Matrices are initially pre-processed and afterwards used as underlying databases to investigate the pattern recognition potential of clustering techniques by considering the information regarding the optimal number of clusters and by measuring the respective internal as well as external performances. The overall research results show that certain cluster separations are recommended by internal and external performance measures by means of a holistic evaluation approach, whereas three of the clustering separations are eliminated based on the evaluation results. [ABSTRACT FROM AUTHOR]

Statistical process control (SPC) has proven to be an effective tool for measuring, controlling, and improving a process through the application of statistical procedures. The most valuable SPC tool is control charts, however, if data assumptions are not met, alterations of the statistical performance of traditional control charts may be expected. In practice, the independence assumption is commonly violated in many discrete and continuous industrial processes. Recently, machine learning has been applied in the field of control chart pattern recognition (CCPR) in order to detect and identify abnormal patterns associated with assignable causes. In several instances, the performance of SVMs has been proven superior to other machine learning algorithms. In this paper, a SVM-GA-based monitoring system is presented for fault identification in AR(1) auto-correlated processes. A two stages classification scheme is presented. The initial classification stage is centered on abnormal pattern identification. The integration of a genetic algorithm in this stage boosts the performance of the SVM by simultaneously optimizing the input feature vector, the classifiers' hyper-parameters, and the window size. A second stage provides additional information on the detected abnormal pattern in order to facilitate the fault diagnosis. The results showed a good accuracy performance on both classification stages. The proposed monitoring system yielded better classification accuracies for positive autocorrelation levels than it did for their negative counterparts. The worst classification accuracies were obtained when autocorrelation was not present. [ABSTRACT FROM AUTHOR]

The supervised dictionary learning methods have made considerable achievements in the field of pattern recognition. In order to make the learned coefficients have intraclass similarity and interclass incoherence, many dictionary learning methods have been proposed using different structured constraints. However, these constraints often have the disadvantages of complexity, time-consuming, and poor interpretability. More importantly, the existing dictionary learning often ignores the inherent structure of the label matrix. In this paper, we propose a dictionary pair learning based on the structured classifier and perform classifier learning and structured coefficients learning simultaneously. Our main idea is to use the extended label matrix and the invertible constraints on the classification transformation matrix to utilize the structure of the label matrix to impose structured constraints on coefficients. Moreover, the l 21 -norm is added to force the analysis dictionary to focus on more important features. Experimental results on multiple databases prove that our proposed method has better classification performance than several state-of-the-art dictionary learning methods. [ABSTRACT FROM AUTHOR]

In this paper, we outline the overview of the problem of intraplate seismicity and summarize our studies aimed at identifying potential earthquake sources in three regions located in continental environments. We study the French Massif Central situated within the West-European platform, the Gujarat area located at the northwestern edge of the Indian shield, and northeast Egypt located in the northeastern part of the African continent. These regions exhibit different levels of seismic activity. The French Massif Central reveals the lowest rate of seismicity, northeast Egypt exposes a low-to-moderate seismicity, and Gujarat reveals moderate seismicity with rare occurrence of strong events. We use a phenomenological approach for identifying possible locations of earthquakes, which is based on the pattern recognition applied to morphostructural data. The approach hypothesizes the nucleation of strong earthquakes at morphostructural nodes forming at the intersections of morphostructural lineaments, which are delineated by morphostructural zoning. Nodes are characterized by the pertinent geophysical and geological parameters, on the basis of which pattern recognition algorithm pinpoints capable nodes, i.e., the nodes where events of a certain sizes may occur. Seismic hazard assessment of continental regions is impeded because their seismic history is normally very pure. In such a situation, information on capable nodes is a necessary input for seismic hazard assessment. The example of northeast Egypt shows the exploiting capable nodes to assess seismic hazard by the Neo-Deterministic Seismic Hazard Assessment Methodology. [ABSTRACT FROM AUTHOR]

In this paper, a Competitive Neural Network circuit based on voltage-controlled memristors is proposed, of which the synapse structure is one memristor (1M). The designed circuit consists of the forward calculation part and the weight updating part. The forward calculation part is designed according to the winner-take-all mechanism, in which the m-LIF model and PMOS transistors with switching characteristics are combined to achieve the lateral inhibition. The weight updating part is designed based on the Hebbian learning rule. By using the voltage controlled switches, only the synaptic memristors connected to the winner output neuron obtained from the forward calculation part are adjusted. The whole circuit does not require the participation of CPU, FPGA or other microcontrollers, providing the possibility to realize computing-in-memory and parallel computing. We perform simulation experiments of unsupervised online learning of 5*3 pixels patterns and 28*28 pixels patterns based on the designed circuit in PSPICE. The changing trend of the network weights during the training phase and the high recognition accuracy in the recognition phase verify the network can effectively learn and recognize different patterns. [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]

Studies recently accomplished on the Enteric Nervous System have shown that chronic degenerative diseases affect the Enteric Glial Cells (EGC) and, thus, the development of recognition methods able to identify whether or not the EGC are affected by these type of diseases may be helpful in its diagnoses. In this work, we propose the use of pattern recognition and machine learning techniques to evaluate if a given animal EGC image was obtained from a healthy individual or one affect by a chronic degenerative disease. In the proposed approach, we have performed the classification task with handcrafted features and deep learning-based techniques, also known as non-handcrafted features. The handcrafted features were obtained from the textural content of the ECG images using texture descriptors, such as the Local Binary Pattern (LBP). Moreover, the representation learning techniques employed in the approach are based on different Convolutional Neural Network (CNN) architectures, such as AlexNet and VGG16, with and without transfer learning. The complementarity between the handcrafted and non-handcrafted features was also evaluated with late fusion techniques. The datasets of EGC images used in the experiments, which are also contributions of this paper, are composed of three different chronic degenerative diseases: Cancer, Diabetes Mellitus, and Rheumatoid Arthritis. The experimental results, supported by statistical analysis, show that the proposed approach can distinguish healthy cells from the sick ones with a recognition rate of 89.30% (Rheumatoid Arthritis), 98.45% (Cancer), and 95.13% (Diabetes Mellitus), being achieved by combining classifiers obtained on both feature scenarios. [ABSTRACT FROM AUTHOR]

Segmentation of dried amnion biomaterial tends to produce invalid (outlier) contour point detections due to texture and colour inhomogeneity of the biomaterial. In this paper, a novel implementation of a non-stationary Bayesian learning process for outlier contour point removal of amnion segmentations is presented. This outlier removal method is independent to algorithms used for the contour detection. The Bayesian process uses a non-stationary kernel to learn a function with complex shape that maps image features in a region-of-interest around each contour point to a discrete output. Based on this output, a contour point can be determined as valid or invalid (outlier). The hyper-parameters of the non-stationary kernel are learned by maximising the marginal likelihood of the combined likelihood of data and the prior of the kernel parameters. Moreover, a novel combination of gradient-ascend and harmonic heuristic search methods is presented to find the optimal hyper-parameters. To validate the method, experiments are conducted to detect and ignore invalid contour points on amnion biomaterial images. A comparison of the proposed method with a logistic regression classification as the baseline is performed. The results show that the proposed method can significantly improve the contour detection by removing outliers and, hence, can reduce waste of uncut biomaterials. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new set of fractional-order continuous orthogonal moments for image representation. These are called fractional-order Jacobi moments (FrJMs) and are defined from the fractional-order orthogonal Jacobi polynomials. We also propose a method for the fast and precise calculation of FrJMs based on recursive calculations of fractional-order Jacobi polynomials and on the separability property of FrJMs. Then, we will derive invariants of FrJMs with respect to rotation, scale and translation (RST) in order to apply them for classification tasks. Just as important, we have presented a systematic parameter selection method for finding the optimal fractional parameter values with respect to pattern recognition applications. Finally, an experimental and comparative study was carried out to test the capacity of FrJMs for the image reconstruction, extraction of global and local image characteristics, invariance to RST, sensitivity to noise, ability to recognize similar grayscale images and the computational times of the new descriptors. The proposed descriptors outperformed the recent orthogonal moments with fractional orders. [ABSTRACT FROM AUTHOR]

In this paper, a new distance measure of three-parameter interval numbers is proposed, which considers not only the particularity of the center of gravity in a three-parameter interval number but also the influence of risk attitudes of decision makers. Some properties of the proposed distance measure are also proved from the perspective of metric spaces. Based on the proposed distance measure of three-parameter interval numbers, a new type of distance measure of LR-type fuzzy numbers is introduced. By considering different left and right shape functions in the proposed distance measure of LR-type fuzzy numbers, some new distance measures of triangular fuzzy numbers and normal fuzzy numbers are obtained. It is shown from some examples that the proposed distance measures of triangular fuzzy numbers and normal fuzzy numbers may overcome drawbacks of some existing similarity measures of triangular fuzzy numbers and normal fuzzy numbers. [ABSTRACT FROM AUTHOR]

This paper introduces a scheme for semi-supervised data representation. It proposes a flexible nonlinear embedding model that imitates the principle of spectral graph convolutions. Structured data are exploited in order to determine nonlinear and linear models. The introduced scheme takes advantage of data graphs at two different levels. First, it incorporates manifold regularization that is naturally encoded by the graph itself. Second, the regression model is built on the convolved data samples that are obtained by the joint use of the data and their associated graph. The proposed semi-supervised embedding can tackle challenges related to over-fitting in image data spaces. The proposed graph convolution-based semi-supervised embedding paves the way to new theoretical and application perspectives related to the nonlinear embedding. Indeed, building flexible models that adopt convolved data samples can enhance both the data representation and the final performance of the learning system. Several experiments are conducted on six image datasets for comparing the introduced scheme with many state-of-art semi-supervised approaches. These experimental results show the effectiveness of the introduced data representation scheme. [ABSTRACT FROM AUTHOR]

Images recognition and classification require an extraction technique of feature vectors of these images. These vectors must be invariant to the three geometric transformations: rotation, translation and scaling. Several authors used the theory of orthogonal moments to extract the feature vectors of images. Jacobi moments are orthogonal moments, which have been widely applied in imaging and pattern recognition. However, the invariance to rotation of Cartesian Jacobi moments is very difficult to obtain. In this paper, we obtain at first a set of transformed orthogonal Jacobi polynomials, called "Adapted Jacobi polynomials". Based on these polynomials, a set of orthogonal moments is presented, named adapted Jacobi moments (AJMs). These moments are orthogonal on the rectangle 0 , N × 0 , M , where N × M is the size of the described image. We also provide a new series of feature vectors of images based on adapted Jacobi orthogonal invariants moments, which are a linear combination of geometric moment invariants, where the latest ones are invariant under rotation, translation and scaling of the described image. Based on k-NN algorithm, we apply a new 2D image classification system. We introduce a set of experimental tests in pattern recognition. The obtained results express the efficiency of our method. The performance of these feature vectors is compared with someones extracted from Hu, Legendre and Tchebichef invariant moments using three different 2D image databases: MPEG7-CE shape database, Columbia Object Image Library (COIL-20) database and ORL database. The results of the comparative study show the performance and superiority of our orthogonal invariant moments. [ABSTRACT FROM AUTHOR]

Orthogonal moments and their invariants to geometric transformations for gray-scale images are widely used in many pattern recognition and image processing applications. In this paper, we propose a new set of orthogonal polynomials called adapted Gegenbauer–Chebyshev polynomials (AGC). This new set is used as a basic function to define the orthogonal adapted Gegenbauer–Chebyshev moments (AGCMs). The rotation, scaling, and translation invariant property of (AGCMs) is derived and analyzed. We provide a novel series of feature vectors of images based on the adapted Gegenbauer–Chebyshev orthogonal moments invariants (AGCMIs). We practice a novel image classification system using the proposed feature vectors and the fuzzy k-means classifier. A series of experiments is performed to validate this new set of orthogonal moments and compare its performance with the existing orthogonal moments as Legendre invariants moments, the Gegenbauer and Tchebichef invariant moments using three different image databases: the MPEG7-CE Shape database, the Columbia Object Image Library (COIL-20) database and the ORL-faces database. The obtained results ensure the superiority of the proposed AGCMs over all existing moments in representation and recognition of the images. [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]

In the current pandemic, smart technologies such as cognitive computing, artificial intelligence, pattern recognition, chatbot, wearables, and blockchain can sufficiently support the collection, analysis, and processing of medical data for decision making. Particularly, to aid medical professionals in the disease diagnosis process, cognitive computing is helpful by processing massive quantities of data rapidly and generating customized smart recommendations. On the other hand, the present world is facing a pandemic of COVID-19 and an earlier detection process is essential to reduce the mortality rate. Deep learning (DL) models are useful in assisting radiologists to investigate the large quantity of chest X-ray images. However, they require a large amount of training data and it needs to be centralized for processing. Therefore, federated learning (FL) concept can be used to generate a shared model with no use of local data for DL-based COVID-19 detection. In this view, this paper presents a federated deep learning-based COVID-19 (FDL-COVID) detection model on an IoT-enabled edge computing environment. Primarily, the IoT devices capture the patient data, and then the DL model is designed using the SqueezeNet model. The IoT devices upload the encrypted variables into the cloud server which then performs FL on major variables using the SqueezeNet model to produce a global cloud model. Moreover, the glowworm swarm optimization algorithm is utilized to optimally tune the hyperparameters involved in the SqueezeNet architecture. A wide range of experiments were conducted on benchmark CXR dataset, and the outcomes are assessed with respect to different measures . The experimental outcomes pointed out the enhanced performance of the FDL-COVID technique over the other methods. [ABSTRACT FROM AUTHOR]