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The profusion of documents production at an exponential rate over the web has made it difficult for the scientific community to retrieve most relevant information against the query. The research community is busy in proposing innovative mechanisms to ensure the document retrieval in a flexible manner. The document classification is a core concept of information retrieval that classifies the documents into predefined categories. In scientific domain, classification of documents to predefined category (ies) is an important research problem and supports number of tasks such as information retrieval, finding experts, recommender systems, etc. In Computer Science, the Association for Computing Machinery (ACM) categorization system is commonly used for organizing research papers in the topical hierarchy defined by the ACM. Accurately assigning a research paper to a predefined category (ACM topic) is a difficult task especially when the paper belongs to multiple topics. In this paper, we exploit the reference section of a research paper to predict the topics of the paper. We have proposed a framework called Category-Based Category Identification (CBCI) for multi-label research papers classification. The proposed approach extracted references from training dataset and grouped them in a Topic-Reference (TR) pair such as TR {Topic, Reference}. The references of the focused paper are parsed and compared in the pair TR {Topic, Reference}. The approach collects the corresponding list of topics matched with the references in the said pair. We have evaluated our technique for two datasets that is Journal of Universal Computer Science (JUCS) and ACM. The proposed approach is able to predict the first node in the ACM topic (topic A to K) with 74% accuracy for both JUCS and ACM dataset for multi-label classification. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease — COVID-19 is a historic catastrophe that has caused many devastating impacts on human life and wellness. Researchers in academia and industry strive to understand the causes of this pandemic disease and find new therapeutics combating it. Consequently, the number of COVID-19 related publications increases rapidly, and it is too difficult for medical researchers and practitioners to keep up with the latest research and development. Literature filtering and categorization, and knowledge discovery can use text mining as a powerful tool. In this paper, we propose a text mining method to explore the categories of COVID-19 related themes and identify the standard methodologies that have been used. We discuss the potential limitations of this preliminary study and present future perspectives related to COVID-19 research. This paper provides an quantitative and qualitative mixed analysis example of using some research papers by data mining method to dig out several hidden information and set up a foundation for data scientists to develop more effective algorithms to deal with COVID-19 related problems. [ABSTRACT FROM AUTHOR]

The paper contains the classification of non-commutative torsion-free associative rings of rank two. Furthermore, torsion-free abelian groups of rank two supporting an associative, but not commutative ring structure are classified. [ABSTRACT FROM AUTHOR]

A literature review on sarcasm detection has been undergone in this research work. To have a meaningful study about the existing works on sarcasm detection, a total of 65 research papers have been analyzed in diverse aspects like the datasets utilized, language, pre-processing technique, type of features, feature extraction technique, machine learning/deep learning-based sarcasm classification. All these papers belong to diverse international as well as national journals. Moreover, the performance of each work in terms of accuracy, F-score and recall will also be manifested. To show the superiority of the works, a comparative evaluation has been undergone in terms of analyzed performances of each of the works. Finally, the works that hold the superior or improved values are furnished. In addition, the current challenges faced by the sarcasm detection system are portrayed, and this will be a milestone for future researchers. [ABSTRACT FROM AUTHOR]

This study aims to present a multiattribute decision-making (MADM) and clustering method to explore the ranking, classifications and evolution mechanisms of the research fronts in the Web of Science Essential Science Indicators (ESI) database. First, bibliometrics are used to reveal the characteristics of the 57 ESI research fronts with more than 40 ESI highly cited papers (ESI-HCPs) for each research front. Second, the eight representative indicators are discovered to get answers to the following two questions: (i) Who publishes the ESI-HCPs that form a research front? and (ii) Where citations to these ESI-HCPs come from on a research front? Next, we investigate the ranking and clusters among the 57 ESI research fronts using the MADM and k -means clustering method and uncover the evolution process of the research fronts in different clusters based on the representative indicators. We also compare the performances of different countries in these research fronts and find that the USA and China are the leading countries in most research fronts. However, the two countries behave differently with regard to the rankings, the classifications and the evolution. [ABSTRACT FROM AUTHOR]

Orchid is a type of plant that grows on land. It is highly valued for its beauty and is cherished by many because of its graceful flower shape, delicate fragrance, vibrant colors, and noble symbolism. Although there are various types of orchids, some of them look similar in appearance and color, making it challenging for people to distinguish them quickly and accurately. The existing methods for classifying orchid species face issues with accuracy due to the similarities between different species and the differences within the same species. This affects their practical use. To address these challenges, this paper introduces an efficient method for classifying orchid species using transfer learning. The main achievement of this study is the successful utilization of transfer learning to achieve accurate orchid species classification. This approach reduces the need for large datasets, minimizes overfitting, cuts down on training time and costs, and enhances classification accuracy. Specifically, the proposed approach involves four phases. First, we gathered a collection of 12 orchid image sets, totaling 12,227 images, through a combination of network sources and field photography. Next, we analyzed the distinctive features present in the collected orchid image sets. We identified certain connections between the acquired orchid datasets and other datasets. Finally, we employed transfer learning technology to create an efficient classification function for orchid species based on these relationships. As a result, our proposed method effectively addresses the challenges highlighted. Experimental results demonstrate that our classification algorithm, which utilizes transfer learning, achieves a classification accuracy rate of 96.16% compared to not using the transfer learning method. This substantial improvement in accuracy greatly enhances the efficiency of orchid classification. [ABSTRACT FROM AUTHOR]

Cervical cancer (CC) has become one of the serious and deadly diseases in women around the world. Predicting a CC is at risk because testing is not possible at the early stage. Doctors predict the types of cancer by collecting cervical cells and by intervening in person. This approach affects the level of prediction due to human negligence, high cost, and is time consuming. In this paper, we propose the automatic cancer classification using deep neural network to overcome this problem. The proposed work has four stages namely, pre-processing, outlier elimination, dimensionality reduction, and classification. Initially, the missing test data should be removed. Second, the inconsistent data are eliminated based on identical values. Third, principal component analysis (PCA) is used to overcome the limitations caused by high-volume data. Finally, enter the reduced size database in the proposed neural network (DNN) classifier and classify the input data as normal or abnormal. We implement the suggested method using Python and we inspect the performance in terms of different metrics. [ABSTRACT FROM AUTHOR]

This paper presents a quantitative analysis of the nonlinearities of the positive predictive value (PPV) and its effect in evaluating two-class pattern classification models with imbalanced datasets. The analysis is made through an expression of the PPV as a function of two other classification ratios that are invariant to the data imbalance —the true positive rate (TPR) and false positive rate (FPR), and σ — the imbalance ratio (IR) of the dataset such that PPV = σ TPR/(σ TPR + FPR). The curvatures of PPV in the three-dimensional TPR–FPR– σ space are studied using the Hessian matrix, from which a saddle-shaped 3D surface in the space is revealed. This paper explores the nonlinear behaviors of PPV around the critical points, identified at FPR = σ TPR on the saddle surface, along with its scaling and sensitivity issues as performance measurements in model evaluation. The effect of the nonlinearities of PPV for the F1 and MCC metrics on imbalanced datasets is also studied. It is warned through the results of this study that the evaluations of classification models could be misleading if without an awareness and understanding of the nonlinearities associated with the PPV and its relevant metrics on imbalanced datasets. [ABSTRACT FROM AUTHOR]

For a rooted cluster algebra (Q) over a valued quiver Q , a symmetric cluster variable is any cluster variable belonging to a cluster associated with a quiver σ (Q) , for some permutation σ. The subalgebra of (Q) generated by all symmetric cluster variables, is called the symmetric mutation subalgebra and is denoted by ℬ (Q). In this paper, we identify the class of cluster algebras that satisfy ℬ (Q) = (Q) , which contains almost every quiver of finite mutation type. In the process of proving the main result, we provide a classification of quivers mutations classes that relates their maximum weights to the shapes of the initial quivers. Furthermore, some properties of symmetric mutation subalgebras are given. [ABSTRACT FROM AUTHOR]

Let G be a nontrivial transitive permutation group on a finite set Ω and recall that an element of G is a derangement if it has no fixed points. Derangements always exist by a classical theorem of Jordan, but there are so-called elusive groups that do not contain any derangements of prime order. In a recent paper, Burness and the author introduced the family of almost elusive groups, which contain a unique conjugacy class of derangements of prime order. In this paper, we complete the classification of the quasiprimitive almost elusive groups. [ABSTRACT FROM AUTHOR]

Target-oriented opinion word extraction and aspect-level sentiment classification are two highly relevant tasks in aspect-based sentiment analysis. Previous studies tend to separate them and focus on one of the tasks, which ignore the connection between opinion word extraction and sentiment classification, and result in the waste of useful connection information. In this paper, we propose a co-extraction model, in which the two tasks are formulated as a sequence labeling problem. The model involves two stacked Bi-LSTM modules and an information interaction component to generate all opinion-polarity pairs of the input sentences simultaneously. The experimental results show that our model achieves advanced results in target opinion word-polarity co-extraction. The performance of both tasks is stronger than the baseline, and the model is of low complexity and high operational efficiency. [ABSTRACT FROM AUTHOR]

The radial basis function neural network (RBFNN) is a widely used tool for interpolation and prediction problems. In this paper, we propose to improve the traditional RBFNN by automatically identifying core neurons in the hidden layer, based on the l 1 regularization. Our proposed approach will greatly reduce the number of neurons required, which will save the memory and also the computational cost. To determine the radial parameter in the RBFs, we propose to use the K -fold cross-validation method. Moreover, the principal component analysis (PCA) method is used to reconstruct the distance between samples for high-dimensional data sets. Numerical experiments are provided to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In this paper, we give a complete classification of cotorsion pairs in m -cluster categories of type A , denoted by A n − 1 m , via certain configurations of m -diagonals, called Ptolemy diagrams. As applications, we classify m -rigid subcategories of A n − 1 m , which gives Jacquet–Malo's classification of m -cluster tilting subcategories of A n − 1 m . When m = 1 , this generalizes the work of Holm et al. [Ptolemy diagrams and torsion pairs in the cluster category of Dynkin type A n , J. Algebraic Combin. 34 (3) (2011) 507–523] for the classification of torsion pairs in cluster categories of type A n − 1 . [ABSTRACT FROM AUTHOR]

In the paper Improved Overlap-Based Undersampling for Imbalanced Dataset Classification with Application to Epilepsy and Parkinson's Disease, the authors introduced two new methods that address the class overlap problem in imbalanced datasets. The methods involve identification and removal of potentially overlapped majority class instances. Extensive evaluations were carried out using 136 datasets and compared against several state-of-the-art methods. Results showed competitive performance with those methods, and statistical tests proved significant improvement in classification results. The discussion on the paper related to the behavioral analysis of class overlap and method validation was raised by Fernández. In this article, the response to the discussion is delivered. Detailed clarification and supporting evidence to answer all the points raised are provided. [ABSTRACT FROM AUTHOR]

Confronted with the fast social development, the speed of garbage generation has increased. Therefore, from every aspect, especially staying green and healthy, it is urgent and vital to ensure accurate and stable garbage classification. In the field of garbage classification, household garbage has a large volume and the classification process still requires manual intervention. Besides, most of the current studies focus on industrial waste and medical waste, while the existing studies on household garbage are not effective enough. Furthermore, these studies are mostly trained and validated in self-built datasets, making the experimental results insufficiently representative and severely limiting the development of research. Aiming to facilitate the study of household garbage classification, this paper proposes a simple yet effective network model (FConvNet) to improve classification performance. In this paper, an effective convolution block with fused convolution is designed to enhance the potential correlation of the features in the spatial dimension, and then a lightweight symmetric structure is constructed to infer more information about the feature representation. The experiments on public datasets have justified the effectiveness of FConvNet with accuracy rates of 95%, 95% and 97%, achieving better performance on public datasets compared to baselines. Additionally, an online system based on the WeChat mini program framework is designed to provide convenient household garbage classification services and enable the collection of uploaded images to perform model optimizations. [ABSTRACT FROM AUTHOR]

An abelian group A is called a T I -group if every associative ring with the additive group A is filial. The filiality of a ring R means that the ring R is associative and all ideals of any ideal of R are ideals in R. In this paper, torsion-free T I -groups are described up to the structure of associative nil groups. It is also proved that, for torsion-free abelian groups that are not associative nil, the condition T I implies the indecomposability and homogeneity. The paper contains constructions of 2 ℵ 0 such groups of any rank from 2 to 2 ℵ 0 which are pairwise non-isomorphic. [ABSTRACT FROM AUTHOR]

In this paper, we study the classification of sequences containing arbitrarily long arithmetic progressions. First, we deal with the question how the polynomial map n s can be extended so that it contains arbitrarily long arithmetic progressions. Under some growth conditions, we construct sequences which contain arbitrarily long arithmetic progressions. Also, we give a uniform and explicit arithmetic progression rank bound for a large class of sequences. Consequently, a dichotomy result is deduced on the finiteness of the arithmetic progression rank of certain sequences. Therefore, in this paper, we see a way to determine the finiteness of the arithmetic progression rank of various sequences satisfying some growth conditions. [ABSTRACT FROM AUTHOR]

Long short-term memory (LSTM) with significantly increased complexity and a large number of parameters have a bottleneck in computing power resulting from limited memory capacity. Hardware acceleration of LSTM using memristor circuit is an effective solution. This paper presents a complete design of memristive LSTM network system. Both the LSTM cell and the fully connected layer circuit are implemented through memristor crossbars, and the 1T1R design avoids the influence of the sneak current which helps to improve the accuracy of network calculation. To reduce the power consumption, the word embedding dimensionality was reduced using the GloVe model, and the number of features in the hidden layer was reduced. The effectiveness of the proposed scheme is verified by performing the text classification task on the IMDB dataset and the hardware training accuracy reached as high as 88.58%. [ABSTRACT FROM AUTHOR]

In this paper, we obtain the classification theorems for 3-dimensional complete λ -translators x : M 3 → ℝ 4 with constant squared norm S of the second fundamental form and constant f 4 in the Euclidean space ℝ 4 . [ABSTRACT FROM AUTHOR]

In this growing world of the internet, most of our daily routine tasks are somehow connected to the internet, from smartphones to internet of things (IoT) devices to cloud networks. Internet users are growing rapidly, and the internet is accessible to everyone from anywhere. Data phishing is a cyber security attack that uses deception to trick internet users to get their content and information. In this attack, malicious users try to steal personal data such as login credentials, credit card details, health care information, etc., of the users on the internet. They exploit users' sensitive information using vulnerabilities. Information stealers are known as phishers. Phishers use different techniques for phishing. One of the most common methods is to direct the users to a false website to enter their login credentials and their details on these phishing sites. Phishing websites look like the original websites. Phishers use these details to get access to the user's accounts and hijack them for monetary purposes. Many internet users fall for this trap of phishing sites and share their personal and sensitive details. In this paper, we will analyze and implement machine learning (ML) techniques to detect phishing attacks. There are different methods to identify phishing attacks, one of them is by checking the uniform resource locator (URL) address using ML. ML is used to teach a machine to differentiate between phishing and original site URLs. There are many different techniques to overcome this attack. This research paper aims to provide accurate and true phishing detection with less time complexity. [ABSTRACT FROM AUTHOR]

Group photo images are everywhere and vary greatly by the shooting scene. Compared with common images, the Image Aesthetic Quality Assessment (IAQI) of group photo pays more attention to the relevant characteristics of the main population. Still, the existing methods do not make further special research on group photos. Therefore, we propose a new concept of group photo styling based on analyzing group photos and photographic theory. Besides that, by comparing and analyzing many group photos, we classify the group photos into five categories. In this paper, the main factors of the head and pose are considered simultaneously, and the method of Group Photo Styling Classification (GPSC) can classify different group photos automatically. To verify the effectiveness of our method, we collected a Group Photo Styling Dataset (GPSD). The dataset contains 998 group photo images, and each image's group photo styling category is marked. The experimental results on GPSD show that the fusion of head features and pose features can classify different group photos well. The accuracy of GPSC reaches 93.9%, much higher than the previous classification model. [ABSTRACT FROM AUTHOR]

Indonesia is one of the archipelago countries located in Asia and it has diverse cultures. In modern society, Indonesian traditional houses have become rare and need to be preserved. This research is conducted to build a digital collection and to develop an image-based automatic recognition system for Indonesian traditional houses. In this paper, the traditional house images are collected in several ways: on-site image captures, receiving images from volunteers, and collecting public images from Google. The dataset is limited to the collection of building shape images, excluding the interior design. The authors implement Convolutional Neural Networks (ConvNets) to build a model for an automatic recognition system. The experiments run some deep network models: VGG, DenseNet, Inception, Xception, MobileNetV2, NasNetMobile, and EfficientNet. The experiments involve 1526 images of 16 classes. EfficientNet-Lite0 outperforms other models and produces the average F1-score and accuracy of 90.1% and 91.8%, respectively. ConvNets also outperform conventional classifiers. [ABSTRACT FROM AUTHOR]

The clinical diagnosis of heart disease in most situations is based on a difficult amalgamation of pathological and clinical information. Because of this complication, there is a significant level of curiosity among many diagnostic healthcare professionals and researchers who are keenly interested in the efficient, accurate, and early-stage forecasting of heart disease. Deep Learning Algorithms aid in the prediction of heart disease. The main focus of this paper is to develop a method for predicting heart disease through Modified Rough K means + + (MRK + +) clustering along with the Restricted Boltzmann Machine (RBM). This paper is categorized into two modules: (1) Propose a clustering component based on Modified Rough K-means + + ; (2) disease prediction based on RBM. The input Cleveland dataset is clustered using the stochastic probabilistic rough k-means + + clustering technique in the module for clustering. The clustered data is acquired and used in the RBM, and this hybrid structure is then used in the heart disease forecasting module. Throughout the testing procedure, the most valid result is chosen from the clustered test data, and the RBM classifier that correlates to the nearest cluster in the test data is based on the smallest distance or similar parameters. Furthermore, the output value is used to predict heart disease. There are three different types of experiments that are performed: In the first experiment comprises modifying the rough K-means + + clustering algorithm, the second experiment evaluates the classification result, and the third experiment suggests hybrid model representation. When the Hybrid Modified Rough k-means + + - RBM model is compared with any single model, it provides the highest accuracy. [ABSTRACT FROM AUTHOR]

In this paper, we prove that all spherically symmetric Landsberg surfaces are Berwaldian. We modify the classification of spherically symmetric Finsler metrics, done by the author in [S. G. Elgendi, On the classification of Landsberg spherically symmetric Finsler metrics, Int. J. Geom. Methods Mod. Phys. 18 (2021)], of Berwald type of dimension n ≥ 3. Precisely, we show that all Berwald spherically symmetric metrics of dimension n ≥ 3 are Riemannian or given by a certain formula. As a simple class of Berwaldian metrics, we prove that all spherically symmetric metrics in which the function ϕ is homogeneous of degree − 1 in r and s are Berwaldian. [ABSTRACT FROM AUTHOR]

This paper addresses radiologists' specific diagnosis of cancer disease effectively using integrated framework of deep learning model. Although several existing diagnosis systems have been adopted by a physician, in few cases, it is not so practical to see the infected area from images in the normal eye. Thus, a fully integrated diagnosis framework for disease detection is proposed to find out the infected area from image using deep learning approaches in this paper. In this proposed framework, various components are designed through deep learning approaches such as detection, segmentation, classification etc. based on mass region. The classification technique is used to classify the disease as either benign or malignant. The vital part of this framework is developed by using a full resolution convolutional network (FrCN) that supports different stages of image processing, especially breast cancer disease. Different experimental evaluation is taken to perform on the accuracy, cross-validation tests, and the comparative testing. Since we have taken 4-fold evaluation, the FrCN performs with an average 98.7% Dice index, 97.8% TS/CSI coefficient, 99.1% overall accuracy, and 98.15% MCC. Our experiments demonstrated that the proposed diagnosis system performs on the deep learning approaches at each segmentation stage and classification with good results. [ABSTRACT FROM AUTHOR]

In classification of fashion article images based on e-commerce image recommendation system, the classification accuracy and computation time cannot meet the actual requirements. Herein, for the first time to our knowledge, we present two diverse image recognition approaches for classification of fashion article images called random-forest method based on genetic algorithm (GA-RF) and Visual Geometry Group-Image Enhancement algorithm (VGG-IE) to solve classification accuracy and computation time problem. In GA-RF, the number of segmentation times and the decision trees are the key factors affecting the classification results. Improved genetic algorithm is introduced into the parameter optimization of forests to determine the optimal combination of the two parameters with minimal manual intervention. Finally, we propose six different Deep Neural Network architectures, including VGG-IE, to improve classification accuracy. The VGG-IE algorithm uses batch normalization and seven kinds training-data augmentation for ease and promotion of learning process. We investigate the effectiveness of the proposed method using Fashion-MNIST dataset and 70 000 pictures, Experimental results demonstrate that, in comparison with the state-of-the-art algorithms for 10 categories of image recognition, our VGG algorithm has the shortest computational time when it satisfies certain classification accuracy. VGG-IE approach has the highest classification accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a group decision making model with individual preferences to investigate the multicriteria ABC inventory classification problem. We first propose a preliminary group decision matrix by means of identifying the individual preference among the classification criteria as a typical decision maker; then analyze the endogenous individual preference structure in terms of exploring the preferential difference and preferential priority, a revised group decision matrix is therefore presented; thirdly, make the use of SMAA-2 to derive the holistic acceptability index for classifying the SKUs. A satisfaction index is developed to manifest the merits of proposed method. A numerical illustration using the data drawn from previous studies is performed to indicate the applicability and feasibility of our method. [ABSTRACT FROM AUTHOR]

It is crucial to correctly assess whether a potential borrower can repay the loan in the credit scoring model. The credit loan data has a serious data imbalance because the number of defaulters is far less than the nondefaulters. However, most current methods for dealing with data imbalance are designed to improve the classification performance of minority data, which will reduce the performance of majority data. For a financial institution, the economic loss caused by the decrease in the classification performance of nondefaulters (majority data) cannot be ignored. This paper proposes an ensemble learning method based on one-class and binary classification (EMOBC) for credit scoring. The purpose is to improve the classification accuracy of the minority class while mitigating the loss of classification accuracy of the majority class as much as possible. EMOBC uses undersampling for the majority class (nondefault samples in credit scoring) and perform binary-class learning on the balanced data to improve the classification accuracy of the minority. To alleviate the decline in classification performance of the majority class, EMOBC uses one-class and binary collaborative classification to train classifiers. The classification result is determined by the average of one-class and binary-class classifiers. The experimental results show that EMOBC has good comprehensive performance compared with the existing methods. [ABSTRACT FROM AUTHOR]

With the continuous development of encryption technology, the share of encrypted traffic in the network is increasing, which brings great challenges to the traditional methods of rule-based traffic identification. Deep learning is becoming an inspiring methodology to solve the problem. Previous studies have confirmed that time characteristics play an important role in Tor traffic classification. We find that there is a similarity of time characteristics among different programs. This paper proposes an end-to-end classification framework: the temporal sequence of data fluctuation network (TSDFN). It first extracts the temporal sequence of data fluctuation in the original flow and then uses the GRU network to learn the hidden temporal features. Experiments on public data sets validate the effectiveness of our proposal over other methods. [ABSTRACT FROM AUTHOR]

Hyperspectral image (HSI) classification is an important topic in remote sensing. In this paper, we propose a new method for HSI classification by using minimum noise fraction (MNF), spatial filtering (SF) and support vector machine (SVM). We use MNF to reduce the dimensionality of a hyperspectral data cube before performing classification. We apply 2D SF to the DR output band images and then use SVM to classify the pixels of the data cube. In this way, both spatial information and spectral information are taken into consideration in the classification. Experimental results show that our MNF+SF method is extremely competitive when compared to several existing classification methods. [ABSTRACT FROM AUTHOR]

This paper analyzes the ability of different machine learning algorithms to find patterns in the levels of gene expression for the correct classification of the five different types of tumors: breast, colon, kidney, lung, and prostate. The machine learning techniques were selected according to the most used algorithms in the related works: Bayesian method, Decision Trees, and K -Nearest Neighbors. Three metrics were applied to test the performance of the classifiers: Precision, Recall, and F 1 -score. The results of Precision of the algorithms were 95.03% (Bayesian), 96.73% (Decision Trees), and 99.52% (K -Nearest Neighbors). A software prototype was developed to classify tumors based on genetic expressions utilizing these three algorithms with satisfactory results. [ABSTRACT FROM AUTHOR]

A disaster is a devastating incident that causes a serious disruption of the functions of a community. It leads to loss of human life and environmental and financial losses. Natural disasters cause damage and privation that could last for months and even years. Immediate steps need to be taken and social media platforms like Twitter help to provide relief to the affected public. However, it is difficult to analyze high-volume data obtained from social media posts. Therefore, the efficiency and accuracy of useful data extracted from the enormous posts related to disaster are low. Satellite imagery is gaining popularity because of its ability to cover large temporal and spatial areas. But, both the social media and satellite imagery require the use of automated methods to avoid the errors caused by humans. Deep learning and machine learning have become extremely popular for text and image classification tasks. In this paper, a review has been done on natural disaster detection through information obtained from social media and satellite images using deep learning and machine learning. [ABSTRACT FROM AUTHOR]

In this paper, we discuss applications of neural networks to recognizing knots and, in particular, to the unknotting problem. One of the motivations for this study is to understand how neural networks work on the example of a problem for which rigorous mathematical algorithms for its solution are known. We represent knots by rectangular Dynnikov diagrams and apply neural networks to distinguish a given diagram's class from a finite family of topological types. The data presented to the program is generated by applying Dynnikov moves to initial samples. The significance of using these diagrams and moves is that in this context the problem of determining whether a diagram is unknotted is a finite search of a bounded combinatorial space. In this way, this paper provides a foundation for further work where the neural network itself will learn to use the Dynnikov moves for knot recognition. Source code of the programs is available at https://github.com/nerusskikh/deepknots. [ABSTRACT FROM AUTHOR]

Network Intrusion Detection Systems (NIDSs) have been proposed in the literature as security tools for detecting anomalous and intrusive network data traffic. However, the existing NIDS frameworks are computation-intensive, thereby making them unsuitable for deployment in resource-constrained networks with limited computational capabilities. This paper aims to address this issue by proposing computationally efficient NIDS framework for detecting anomalous data traffic in resource-constrained networks. The proposed NIDS framework uses an ensemble-based classifier model comprising multiple classifiers, which enables it to achieve high accuracy and detection rate across a wide range of low-footprint and stealth network attacks. The proposed framework also uses feature scaling and dimensionality reduction techniques to minimize the overall computational overhead. The proposed framework consists of two stages. In the first stage, four distinct base-level classifiers are utilized. The classification probabilities of the first stage are used in the modified meta-level classifier. The modified meta-level classifier is trained on the class probabilities of the base-level classifiers combined using a novel proposed probability function. The performance of the proposed NIDS framework is evaluated on a proprietary testbed dataset and two benchmark datasets namely CICIDS-2017 and UNSW-NB15. The results reveal that the proposed NIDS framework provides better performance than the existing NIDS frameworks in terms of false positive rate, despite using a significantly lower number of input features for its analysis. [ABSTRACT FROM AUTHOR]

Let R be a ring (not necessarily commutative) with identity 1 and let CL (R) be its clean graph. In this paper, we investigate the genus number of the compact Riemann surface in which CL (R) can be embedded and explicitly determine all commutative rings R (up to isomorphism) such that CL (R) has genus at most two. It is shown that for any Artinian ring R , CL (R) is a projective graph if and only if R is isomorphic to F 4. Furthermore, we determine all isomorphism classes of commutative rings whose clean graphs have crosscap two. [ABSTRACT FROM AUTHOR]

In this paper, we study Whittaker modules over the N = 2 Neveu–Schwarz algebra 픤. We first classify all simple finite-dimensional modules over the positive part of 픤. Motivated by this classification, we define Whittaker modules and investigate their simplicity. Finally, we also study the simplicity of the quotient modules of the universal Whittaker module if it is not simple. [ABSTRACT FROM AUTHOR]

In many modern machine learning (ML) models, attention mechanisms (AMs) play a crucial role in processing data and identifying significant parts of the inputs, whether these are text or images. This selective focus enables subsequent stages of the model to achieve improved classification performance. Traditionally, AMs are applied as a preprocessing substructure before a neural network, such as in encoder/decoder architectures. In this paper, we extend the application of AMs to intermediate stages of data propagation within ML models. Specifically, we propose a generalized attention mechanism (GAM), which can be integrated before each layer of a neural network for classification tasks. The proposed GAM allows for at each layer/step of the ML architecture identification of the most relevant sections of the intermediate results. Our experimental results demonstrate that incorporating the proposed GAM into various ML models consistently enhances the accuracy of these models. This improvement is achieved with only a marginal increase in the number of parameters, which does not significantly affect the training time. [ABSTRACT FROM AUTHOR]

Graph clustering is an essential task in data analysis. Recently, there has been a notable trend in the application of deep learning in graph clustering. However, it is worth noting that existing deep graph clustering methods primarily focus on the topological information of nodes while overlooking the use of attribute information in the learned feature representations. In addition, they do not address the consistency of the space distribution in attribute and structure clustering results. To tackle these critical issues, a novel structured deep graph clustering network with consistency constraint (CC-DGC) is proposed. The network first constructs an autoencoder to learn and transmit hierarchical attribute information to the graph autoencoder (GAE). Subsequently, the GAE integrates the received hierarchical attribute information with extracted topological information to generate enhanced clustering representations. Moreover, this paper designs a consistency constraint module to promote consistency between the autoencoder and the GAE by optimizing the cluster space distributions they produce. Finally, the feature extraction and clustering classification processes are synchronized and optimized in a self-supervised manner within a unified framework. Extensive experiments illustrate that the proposed CC-DGC demonstrates superiority over the state-of-the-art deep graph clustering methods on five benchmark datasets. [ABSTRACT FROM AUTHOR]

The growing importance of time series information measure raises questions about how to effectively cluster a large number of nonlinear complex time series data and accurately extract more hidden information from them. In this paper, a clustering measurement and classification method for complex time series, the symmetrical exponential Tsallis relative information (SETRI) measure, is proposed, which aims to address these problems. The intrinsic characteristics of different types of time series information could be validly identified by this method. The modified multidimensional scaling (MDS) method, based on the SETRI measure, has the ability to display the data in the form of graphs for intuitive exhibition and accomplish the process of dimension reduction. The introduction of weighted permutation patterns allows a higher-accuracy classification not only for the time series dissimilarity quantification, but also for avoiding dispensable errors. Besides, the feasibility of the modified MDS classification method is visually and quantitatively verified by the simulated and real-world data. Compared with other MDS methods, the proposed method has better performance, which is reflected in the validity and rationality of the clustering results, thus further verifying the feasibility of the proposed method. Therefore, the new results will be helpful to develop complex data clustering and dimensionality reduction methods. [ABSTRACT FROM AUTHOR]

A semidualizing module is a generalization of Grothendieck's dualizing module. For a local Cohen–Macaulay ring R, the ring itself and its canonical module are always realized as (trivial) semidualizing modules. Reasonably, one might ponder the question; when do nontrivial examples exist? In this paper, we study this question in the realm of numerical semigroup rings and, up to multiplicity 9, completely classify which of these rings possess a nontrivial semidualizing module. Using this classification, for each integer n > 8 , we construct a numerical semigroup ring of multiplicity n which admits a nontrivial semidualizing module. [ABSTRACT FROM AUTHOR]

Univariate regression trees have representation problems for non-orthogonal regression functions. Ensembles of univariate regression trees have better representational power. In some cases, weighted ensembles have shown better performance than unweighted ensembles. In this paper, we study the properties of ensembles of regression trees by using regression classification models. We propose a theoretical framework to study the representational power of infinite-sized weighted ensembles, consisting of randomised finite-sized regression trees. We show for some datasets that the weighted ensembles may have better representational power than unweighted ensembles, but the performance is highly dependent on the weighting scheme and the properties of datasets. Our model cannot be used for all the datasets. However, for some datasets, we can accurately predict the experimental results of ensembles of regression trees. [ABSTRACT FROM AUTHOR]

In this paper, we study the interesting open problem of classifying the minimal Lagrangian submanifolds of dimension n in complex space forms with semi-parallel second fundamental form. First, we completely solve the problem in cases n = 2, 3, 4. Second, supposing further that the scalar curvature is constant for n ≥ 5, we also give an answer to the problem by applying the classification theorem of [F. Dillen, H. Li, L. Vrancken and X. Wang, Lagrangian submanifolds in complex projective space with parallel second fundamental form, Pacific J. Math. 255 (2012) 79–115]. Finally, for such Lagrangian submanifolds in the above problem with n ≥ 3, we establish an inequality in terms of the traceless Ricci tensor, the squared norm of the second fundamental form and the scalar curvature. Moreover, this inequality is optimal in the sense that all the submanifolds attaining the equality are completely determined. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI)-based approaches are crucial in computer-aided diagnosis (CAD) for various medical applications. Their ability to quickly and accurately learn from complex data is remarkable. Deep learning (DL) models have shown promising results in accurately classifying Alzheimer's disease (AD) and its related cognitive states, Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI), along with the healthy conditions known as Cognitively Normal (CN). This offers valuable insights into disease progression and diagnosis. However, certain traditional machine learning (ML) classifiers perform equally well or even better than DL models, requiring less training data. This is particularly valuable in CAD in situations with limited labeled datasets. In this paper, we propose an ensemble classifier based on ML models for magnetic resonance imaging (MRI) data, which achieved an impressive accuracy of 96.52%. This represents a 3–5% improvement over the best individual classifier. We evaluated popular ML classifiers for AD classification under both data-scarce and data-rich conditions using the Alzheimer's Disease Neuroimaging Initiative and Open Access Series of Imaging Studies datasets. By comparing the results to state-of-the-art CNN-centric DL algorithms, we gain insights into the strengths and weaknesses of each approach. This work will help users to select the most suitable algorithm for AD classification based on data availability. [ABSTRACT FROM AUTHOR]

Symbolically representing the knowledge acquired by a neural network is a profound endeavor aimed at illuminating the latent information embedded within the network. The literature offers a multitude of algorithms dedicated to extracting symbolic classification rules from neural networks. While some excel in producing highly accurate rules, others specialize in generating rules that are easily comprehensible. Nevertheless, only a scant few algorithms manage to strike a harmonious balance between comprehensibility and accuracy. One such exemplary technique is the Rule Extraction from Neural Network Using Classified and Misclassified Data (RxNCM) algorithm, which adeptly generates straightforward and precise rules outlining input data ranges with commendable accuracy. This article endeavors to enhance the classification performance of the RxNCM algorithm by leveraging ensemble technique. Ensembles, a burgeoning field, focus on augmenting classifier performance by harnessing the strengths of individual classifiers. Extraction of rules through neural network ensembles is relatively underexplored, this paper bridges the gap by introducing the Rule extraction using Neural Network Ensembles (RENNE) algorithm. RENNE is designed to refine the classification rules derived from the RxNCM algorithm through ensemble strategy. Specifically, RENNE leverages patterns correctly predicted by an ensemble of neural networks during the rule generation process. The efficacy of the algorithm is validated using seven datasets sourced from the UCI repository. The outcomes indicate that the proposed RENNE algorithm outperforms the RxNCM algorithm in terms of performance. [ABSTRACT FROM AUTHOR]

Nowadays, imbalanced data classification is a hot topic in data mining and recently, several valuable researches have been conducted to overcome certain difficulties in the field. Moreover, those approaches, which are based on ensemble classifiers, have achieved reasonable results. Despite the success of these works, there are still many unsolved issues such as disregarding the importance of samples in balancing, determination of proper number of classifiers and optimizing weights of base classifiers in voting stage of ensemble methods. This paper intends to find an admissible solution for these challenges. The solution suggested in this paper applies the support vector data descriptor (SVDD) for sampling both minority and majority classes. After determining the optimal number of base classifiers, the selected samples are utilized to adjust base classifiers. Finally, genetic algorithm optimization is used in order to find the optimum weights of each base classifier in the voting stage. The proposed method is compared with some existing algorithms. The results of experiments confirm its effectiveness. [ABSTRACT FROM AUTHOR]

In order to improve the accuracy and precision of online learning-based collision detection methods, an online active ensemble learning for robot collision detection (OAELRCD) is proposed in this paper. The OAELRCD consists of two key components: (1) an ensemble learning method to combine several base classifiers in order to improve the accuracy and precision of collision detection, (2) an active learning algorithm to reduce the number of training samples in order to realize online training and learning when the environment changes. We evaluate the proposed OAELRCD on one robot arm in dynamic environments with moving workspace obstacles, showing that the proposed OAELRCD outperforms state-of-the-art online learning-based method and geometric collision checkers. Compared to the state-of-the-art online learning-based method for robot collision detection in dynamic environments, the proposed OAELRCD provides noticeable improvements in TPR, AUC, Accuracy and TNR. Compared to state-of-the-art geometric collision checkers, with the proposed OAELRCD, collision checks are faster. [ABSTRACT FROM AUTHOR]

Aiming at the poor convergence of the current sketch image classification and unable to meet the growing network needs, a classification and model method of convolution feature in sketch image based on deep learning is proposed. Based on the classification principle of deep learning, a classification experiment was carried out on the semantic features of sketch works through the analysis of convolution neural network, convolution feature model, convolution and sketch extraction boundary. The experimental results show that the proposed convolution classification and recognition method is better than the traditional classification method and has higher accuracy in dimensionality reduction and error rate detection than the traditional method. It can better meet the needs of network intelligent processing of sketch image feature classification. [ABSTRACT FROM AUTHOR]

The generalization is one of the main concerns of machine learning method. k-SVCR is an important multi-class classification algorithm, but there is no theoretical analysis on the generalization of k-SVCR algorithm up to now. Therefore, in this paper, we consider multi-class support vector classification and regression (MSVCR) algorithm. We first establish the generalization bounds of MSVCR based on uniformly ergodic Markovian chain (u.e.M.c.) samples, and obtain its fast learning rate of MSVCR. As applications, we estimate the generalization of MSVCR for independent and identically distributed (i.i.d.) observations and strongly mixing observations, respectively. We also propose a new MSVCR algorithm based on q-times Markovian resampling (MSVCR-Mar-q). The experimental studies indicate that compared to the classical k-SVCR and other multi-class SVM algorithms, the proposed algorithm not only has smaller misclassification rate, but also has less sampling and training total time. [ABSTRACT FROM AUTHOR]

In view of the problem of excessive noise in financial time series, this paper proposes a nearest neighbor dynamic time warping classification method for financial time series based on the optimal smooth denoising model (osdDTW2). First, the optimal smooth denoising model is improved, and then the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method is used to decompose the time series signal. Then, the improved optimal smooth denoising model is used to construct a low-pass filter to do the denoising of the time series. After being denoised, the time series are aligned by dynamic time warping (DTW), Finally, the nearest neighbor method is used for classification. This paper also uses the UCR datasets to verify the effectiveness of the proposed method and applies it method to financial time series classification. The experimental results suggest that osdDTW2 ( α sm = 0. 9) can improve the effectivness of the benchmark algorithm (DTW) to some extent. [ABSTRACT FROM AUTHOR]

In recent years, the workload of security offers has increased along with the requirement of anti-terrorism. In the paper, a series of evaluation index of security inspection based on the EEG signals of the security officers were proposed to improve the accuracy of dangerous instances detection and decrease the workload of the officers. We performed an experiment to record the EEG data of security officers when they were watching the picture with or without the dangerous item in the uncovered and obscured scenes. Brain network analysis based on graph theory was applied to generate the indexes from the EEG induced by the parcel picture of security inspection, and is a new perspective on the classification of the parcel composition. The paper studied the low-frequency, multi-channel experts EEG signals, calculated the phase locking value (PLV) between every two channels to construct the topological functional brain network (FBN). The appropriate binary FBNs were obtained by setting the thresholds, and then the complex brain network parameters were estimated by the graph-theoretic methods, which were used for classification with 10-fold cross-validation and the average accuracy was 83.3 ± 97.78%. The method was effectively applied to the substance classification and would further improve the recognition accuracy of the target by combining this method with the existing detection technology. [ABSTRACT FROM AUTHOR]