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In forensic document analysis, the authenticity of a document must be properly checked in the context of suspected forgery. Hyperspectral Imaging (HSI) is a non-invasive way of detecting fraudulent papers in a multipage document. The occurrence of a forged paper in a multi-page document may have a substantial difference from rest of the papers in its age, type, color, texture, and so on. Each pixel in an HSI data can be used as the material fingerprint for the spatial point it corresponds to. Hence, hyperspectral data of paper samples made of the same substance have similar characteristics and can be grouped into a single cluster. Similarly, paper samples made of different substances have different spectral properties. This paper relies on this heuristic and proposes a tensor based clustering framework for hyperspectral paper data, with an application to detect the forged papers in multi-page documents. Information embedded in the hyperspectral patches of the papers to be clustered is arranged into individual lateral slices of a third-order tensor in this framework. Further, this work employs the self-expressiveness property of submodules and an objective function is formulated to extract self-expressive representation tensor with low multirank and f-diagonal structure. Objective function of the proposed method incorporates $l_{\frac {1}{2}}$ -induced Tensor Nuclear Norm (TNN) and $l_{\frac {1}{2}}$ regularization to impart better low rankness and f-diagonal structure to the representation tensor. Experimental results of the proposed method were compared to the state-of-the-art subspace clustering approaches. The results demonstrate improved performance of the proposed method over the existing clustering algorithms.

Food loss ans waste, referred next as FLW, has a great extent on the economic and behavioural patterns of consumption. In this paper, we aim to present statistical univariate and multivariate analyses based on a statistical study run during a period of several months in the Romanian territory. In this matter, the main purpose of the analysis developed in this paper is to determine several patterns of FLW phenomenon. This is made based on a methodology which comprises two main sections: the univariate analysis, consisting in statistical determinations of indicators and direct observation, and the multivariate analysis, related to the clustering analysis. The clustering analysis is based on the usage of hierarchical method, which has as final result the determination of clusters describing patterns of consumption related to food loss and waste. After the analysis was run, five main clusters related to food waste behaviour patterns were determined, with slight differences between food consumption habits, but with various combinations between the parameters taken into consideration. The obtained clusters offer important information for policymakers and stakeholders aiming to customize interventions and programs to target specific demographic groups or segments of the population. [ABSTRACT FROM AUTHOR]

By the end of 2020, over 16,300 Massive Open Online Courses (MOOCs) from 950 universities worldwide had enrolled over 180 million students. Interest in MOOCs has been matched by significant research on the topic, including a considerable number of reviews. This study uses Machine Learning techniques and human expert supervision to generate a comprehensive systematic literature mapping review that overcomes some limitations of the traditional ones and provides a broader overview of the content and main topics studied in the specialized literature devoted to MOOCs. The sample consisted of 6320 publications automatically classified within six research topics, denominated by human experts: institutional approach, pedagogical approach, evaluation, analytics, participation, and educational resources. The content analysis of the topics identified was conducted using visual network analysis, which supported the identification of different thematic sub-clusters and endorsed the classification. Results from the review show that the lowest production of MOOC papers is within the topics of the pedagogical approach and educational resources. In contrast, participation and evaluation are the most frequent ones. In addition, the most cited papers are on the topics of analytics and resources, being the pedagogical approach and the institutional approach the less cited. This highlights the need for more MOOC research from a pedagogical perspective and calls upon the presence of educators. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a very famous network because of its many applications. IoT network has an integration of large-scale IoT devices that generate data. These IoT devices are very low power computing devices due to which they have a low level of communication. These devices construct data and transmit the data to the base station via intermediate IoT devices. The base station gathers and integrates the data and sends it to the administrator for further processing. The data attains the base station using various routing algorithms with the goal of low power consumption. When discussing low power IoT devices, power efficiency is an important performance measurement when creating a routing algorithm. This paper proposes a Minimum Power Consumption Routing (MPCR) algorithm using Hierarchical Fuzzy Logic Clustering (HFLC) algorithm for IoT networks. The MPCR with HFLC algorithm is an energy-efficient algorithm because of its lower power consumption for the cluster by aggregating the data within the cluster head and decreasing the number of data transmissions to the base station. In this paper, the cluster formation and cluster-head selection are explained, and a simulation has been conducted. In addition, the proposed algorithm is compared with the existing algorithms based on different metrics such as throughput, packet delivery ratio, and energy consumption of the network. The experimental results show that the proposed MPCR with the HFLC algorithm provides high throughput and packet delivery ratio and reduces energy consumption more efficiently than other existing algorithms. [ABSTRACT FROM AUTHOR]

New discoveries and knowledge are summarized in thousands of published papers per year per scientific domain, making it incomprehensible for scientists to account for all available knowledge relevant for their studies. In this paper, we present ACCORDION (ACCelerating and Optimizing model RecommenDatIONs), a novel methodology and an expert system that retrieves and selects relevant knowledge from literature and databases to recommend models with correct structure and accurate behavior, enabling mechanistic explanations and predictions, and advancing understanding. ACCORDION introduces an approach that integrates knowledge retrieval, graph algorithms, clustering, simulation, and formal analysis. Here, we focus on biological systems, although the proposed methodology is applicable in other domains. We used ACCORDION in nine benchmark case studies and compared its performance with other previously published tools. We show that ACCORDION is: comprehensive, retrieving relevant knowledge from a range of literature sources through machine reading engines; very effective, reducing the error of the initial baseline model by more than 80%, recommending models that closely recapitulate desired behavior, and outperforming previously published tools; selective, recommending only the most relevant, contextspecific, and useful subset (15%-20%) of candidate knowledge in literature; diverse, accounting for several distinct criteria to recommend more than one solution, thus enabling alternative explanations or intervention directions. [ABSTRACT FROM AUTHOR]

With the rapid development of internet communication and the wide application of intelligent terminal, moving the cache to the edge of the network is an effective solution to shorten the delay of users accessing content. However, the existing cache work lacks the comprehensive consideration of users and content, resulting in low cache hit ratio and low accuracy of the whole system. In this paper, the authors propose a collaborative caching model that considers both user request content and content prediction, so as to improve the caching performance of the whole network. Firstly, the model uses the clustering algorithm based on Akike information criterion to cluster users. Then, combined with the clustering results, echo state network is used as the machine learning framework to predict the content. Finally, the cache contents are selected according to the prediction results and cached in the cache unit of the small base station. Simulation results show that compared with the existing cache algorithms, the proposed method has obvious improvement in cache hit ratio, accuracy, and recall rate. [ABSTRACT FROM AUTHOR]

Reverse mortgage is one of the products (perhaps the main one) that is good to obtain additional income by using the habitual residence as collateral. The main objective of this paper is to analyse the effects that reverse mortgage contracting has on household finances over the lifetime of a family according to the socioeconomic group to which it belongs in Spain. Four indicators are employed to measure the immediate and long-term effects. We use a stochastic model with a double source of randomness, survival and entry into dependency, and apply it to the three socioeconomic groups obtained with cluster methodology from the 2017 Spanish Household Financial Survey data. We conclude that the effects are very different depending on the group: regarding only the effects of hiring a reverse mortgage on the income of the family, widowed women aged between 81 and 85 years, with low income and expenses as well as little net wealth, and a habitual residence that represents half of her net wealth (Cluster 1) are the most benefited; considering that the highest impact indicators are on the probability of illiquidity and on the value of lack of liquidity, the use of reverse mortgages benefits more the families in Cluster 3 (high income and expenses and really high net wealth, head of household aged between 76 and 80 years) and less the families in Cluster 2 (medium income, net wealth and expenses, head of household aged between 65 and 75 years). [ABSTRACT FROM AUTHOR]

Research productivity and impact (RPI) is commonly measured through citation analysis, such as the h-index. Despite the popularity and objectivity of this type of method, it is still difficult to effectively compare a number of related researchers in terms of various citation-related statistics at the same time, such as average cites per year/paper, the number of papers/citations, h-index, etc. In this work, we develop a method that employs information visualization technology, and examine its applicability for the assessment of researchers' RPI. Specifically, our prototype, a visualizing research productivity and impact (VisualRPI) system, is introduced, which is composed of clustering and visualization components. The clustering component hierarchically clusters similar research statistics into the same groups, and the visualization component is used to display the RPI in a clear manner. A case example using information for 85 information systems researchers is used to demonstrate the usefulness of VisualRPI. The results show that this method easily measures the RPI for various performance indicators, such as cites/paper and h-index. [ABSTRACT FROM AUTHOR]

Traditional metrics may not adequately assess performance in certain situations, whereas the Area Under Curve (AUC) offers a comprehensive perspective by considering both sensitivity and specificity. This method enhances interpretability, addresses limitations, and promotes the development of robust clustering algorithms. In unsupervised learning, utilizing AUC is a significant method for improving the precision and accuracy of machine learning models. Our work is inspired by several recent related works that implement approaches to manage the challenges of developing new metrics that can effectively assess and evaluate the performance of clustering algorithms. The research question relies on the concept of using an optimal metric for model evaluation of classification and clustering. Therefore, the paper investigates the use of the classification metric AUC for clustering validation purposes. The methodology we adopt is a hybrid clustering model because such a technique offers a robust model by combining the strengths of each model. The linkage approach directly impacts the clustering results, so we give significant attention to this feature in our implementation. Among the various linkage methods, we utilized single and average linkages. The Manhattan and Euclidean metrics are the distance measures used in this work. Thus, our contribution is to explore the benefit of using linkages and distance measurement in clustering with the help of the AUC metric. In addition, the entire proposed work and the contributions of this paper are evaluated and applied to the NSL-KDD dataset. Based on the proposed approach of using AUC with clustering, the Detection Rate (DR), False Alarm Rate (FAR), and other criteria are chosen to examine the model's results and capabilities. [ABSTRACT FROM AUTHOR]

The problem of image-based detection of the variety of beans, using artificial intelligence, is currently dealt with by scientists of various profiles. The idea of this paper is to show the possibility of applying different types of distances, primarily those that are fuzzy metrics, in clustering models in order to improve existing models and obtain more accurate results. The paper presents the method of variable neighborhood search, which uses both standard and fuzzy t-metrics and dual fuzzy s-metrics characterized by appropriate parameters. By varying those parameters of the fuzzy metric as well as the parameters of the metaheuristic used, we have shown how it is possible to improve the clustering results. The obtained results were compared with existing ones from the literature. The criterion function used in clustering is a fuzzy metric, which is proven in the paper. [ABSTRACT FROM AUTHOR]

Current edge devices for neural networks such as FPGA, CPLD, and ASIC can support low bit-width computing to improve the execution latency and energy efficiency, but traditional linear quantization can only maintain the inference accuracy of neural networks at a bit-width above 6 bits. Different from previous studies that address this problem by clipping the outliers, this paper proposes a two-stage quantization method. Before converting the weights into fixed-point numbers, this paper first prunes the network by unstructured pruning and then uses the K-means algorithm to cluster the weights in advance to protect the distribution of the weights. To solve the instability problem of the K-means results, the PSO (particle swarm optimization) algorithm is exploited to obtain the initial cluster centroids. The experimental results on baseline deep networks such as ResNet-50, Inception-v3, and DenseNet-121 show the proposed optimized quantization method can generate a 5-bit network with an accuracy loss of less than 5% and a 4-bit network with only 10% accuracy loss as compared to 8-bit quantization. By quantization and pruning, this method reduces the model bit-width from 32 to 4 and the number of neurons by 80%. Additionally, it can be easily integrated into frameworks such as TensorRt and TensorFlow-Lite for low bit-width network quantization. [ABSTRACT FROM AUTHOR]

Most cybersecurity frameworks are based on three major components such as confidentiality, integrity, and availability. All these components have their parameters that are used to secure network nodes. But finding the most cyber secure node in a network needs a measurement method. The aim of the paper is to offer a model that can be used to find the most secure network nodes considering these cybersecurity components and their parameters. This is achieved by modelling numeric values of respective weights for parameters of confidentiality, integrity, and availability. The model is applied to a simulated environment where random values standing for cybersecurity parameters are given to 30 wireless network nodes that are used as an example. Then the weighted values are processed with Python programming language by giving the most secure nodes according to needed cybersecurity components. This model can be used to recommend the right network node that can be used to deploy services securely while avoiding potential vulnerabilities and cyber-attacks. [ABSTRACT FROM AUTHOR]

Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry's predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model's categories, the data's temporality, field, and name, the dataset's type, predictive analytics (classification, clustering, or prediction), the models' input and output parameters, the performance metrics, the optimal model, and the model's benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries. [ABSTRACT FROM AUTHOR]

The aim of this paper is to examine the impact of a country's entry into the monetary union on its economic variables growth rate of real GDP as well as on GDP per capita growth for the period from 2010 to 2020. The clustering method and the ANFIS method were used in the data analysis. A total of two cluster analyses were performed. The first cluster includes countries that joined the EU in 2004 and became EZ members by 2010. The second cluster refers to those countries that joined the EU in 2004 but are not yet members of the EZ. For the first individual cluster analysis two models were analysed and for the second individual cluster three models were analysed using the ANFIS method. As expected, the results showed that GDP growth is connected with trade, inflation and gross investments in fixed capital in the observed countries, while GDP per capita is connected with unemployment, interest rates and public debt. With regard to GDP growth, the difference between countries that are in the eurozone and those that are not is not significant, which is in line with other studies. [ABSTRACT FROM AUTHOR]

Superpixel segmentation can get the middle features in image processing, effectively reduce the dimensionality of the image, and is widely used in image processing fields. To get the regular and compact superpixels in real-time, a superpixel segmentation algorithm based on image density is proposed in this paper. Firstly, the image is uniformly divided according to the number of superpixels to be obtained. Secondly, to get the clustering ability of the pixels, the density image is produced. Thirdly, the seed is chosen in each sub-region block according to the density and then the superpixels are obtained by clustering. During the clustering process, the pixel around the seed should be added into the superpixel if it meets the conditions, and the small supeipixels are merged into the big superpixels around them. Finally, the result shows that the proposed algorithm has the best segmentation effect, and a good balance in accuracy, regularity, and time cost. [ABSTRACT FROM AUTHOR]

An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data are presented as images, identifying properties that characterize a disease becomes far more challenging. A common strategy involves extracting features from the images and analyzing their occurrence in healthy versus pathological images. A limitation of this approach is that the ability to gain new insights into the disease from the data is constrained by the information in the extracted features. Typically, these features are manually extracted by humans, which further limits the potential for new insights. To overcome these limitations, in this paper, we propose a novel framework that provides insights into diseases without relying on handcrafted features or human intervention. Our framework is based on deep learning (DL), explainable artificial intelligence (XAI), and clustering. DL is employed to learn deep patterns, enabling efficient differentiation between healthy and pathological images. Explainable artificial intelligence (XAI) visualizes these patterns, and a novel "explanation-weighted" clustering technique is introduced to gain an overview of these patterns across multiple patients. We applied the method to images from the gastrointestinal tract. In addition to real healthy images and real images of polyps, some of the images had synthetic shapes added to represent other types of pathologies than polyps. The results show that our proposed method was capable of organizing the images based on the reasons they were diagnosed as pathological, achieving high cluster quality and a rand index close to or equal to one. [ABSTRACT FROM AUTHOR]

This paper proposes a method to reduce seismogram variability as a determining factor in its interpretation, processing, and clustering. By introducing the concept of single fluctuations (SFs), the seismogram can be parsed into a sequence of random impulses with subsequent ordering. This rearrangement of SFs, if they are assigned by positive integers, represents the formal mapping of a regular string of integers into a random one, which can be represented with a bipartite random graph (bigraph). Due to its specific randomness, such a bigraph is considered a new type of random balanced bigraph. The R-envelope and RQ-envelope, its equidistant version, are defined by tracing the peak envelope over ordered SFs. The equivalence and complementariness of the RQ and bigraph are considered and discussed, forming a combined characteristic of the seismogram. The R/RQ provided a considerable reduction in seismogram variability, which was confirmed by creating and analyzing an ensemble of RQ from several seismograms. In the RQ domain, distance is defined as a possible basis for metrics and clustering, but the ensemble variability was quite narrow and not as suitable for this purpose. Otherwise, the ensemble shows high redundancy hidden in the seismogram population. As for the bigraph, the mesh of its edges is structuralized in bundles, forming a skeleton, which reflects the internal structural content of the seismogram. The distance over the domain of bigraphs is proposed to show the possibility of clustering. This means that only a combined RQ and bigraph provides a suitable frame for seismogram representation with reduced variability and, thus, the potential for more effectively organizing seismic databases and a deeper interpretation of seismograms; therefore, RQs and bigraphs can be considered as a transform of a seismogram. Many aspects of these concepts are thoroughly discussed. The similarity between concepts of SF and wavelets is briefly considered. This very complex theme is new and promises broad further research. All issues considered in the paper are abundantly illustrated. [ABSTRACT FROM AUTHOR]

Bridge structures are susceptible to environmental and operational variations (EOVs). Improperly handling these influences may result in incorrect assessments of the bridge's health condition. Blind source separation (BSS) techniques show promising potential in suppressing the effects of EOVs. However, major challenges such as high data variability, difficulty in parameter selection, lack of reliable decision thresholds, and practical engineering validation have seriously hindered the application of such techniques in bridge health monitoring. Consequently, this paper proposes a new method for bridge damage detection that combines complexity pursuit (CP) and extreme value theory (EVT). This method first uses the exponentially weighted moving average (EWMA) technique to preprocess the measured modal frequencies. The CP algorithm and information entropy are then used to extract structural damage sources from the preprocessed data automatically. Based on the extracted structural damage sources, the damage index (DI) is defined using k-means clustering and Euclidean distance. Following that, the generalized extreme value (GEV) distribution is used to fit the DI data under the normal condition of the bridge, and the damage detection threshold is given according to the fitted distribution. Benchmark data of the KW51 railway bridge are considered to verify the effectiveness of the proposed method along with several comparative studies. The results show that even under strong EOV influences, the proposed method still maintains good damage detection accuracy and robustness, and its effectiveness is superior to some well-known damage detection methods. [ABSTRACT FROM AUTHOR]

Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies. [ABSTRACT FROM AUTHOR]

Advancements in virtualization technology have led to better utilization of existing infrastructure. It allows numerous virtual machines with different workloads to coexist on the same physical server, resulting in a pool of server resources. It is critical to understand enterprise workloads to correctly create and configure existing and future support in such pools. Managing resources in a cloud data center is one of the most difficult tasks. The dynamic nature of the cloud environment, as well as the high level of uncertainty, has created these challenges. These applications' diverse Quality of Service (QoS) requirements make data center management difficult. Accurate forecasting of future resource demand is required to meet QoS needs and ensure better resource utilization. Consequently, data center workload modeling and categorization are needed to meet software quality solutions cost-effectively. This paper uses traces of Bitbrain's data to characterize and categorize workload. Clustering (K Means and Gaussian mixture model) and Classification strategies (K Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, and Support Vector Machine) characterize and model the workload traces. K Means shows better results as compared to GMM when compared to the Calinski Harabasz index and Davies-Bouldin score. The results showed that the Decision Tree achieves the maximum accuracy of 99.18%, followed by K Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM) Logistic Regression (LR), Multi-Layer Perceptron (MLP), and Back Propagation Neural Networks. [ABSTRACT FROM AUTHOR]

This paper presents the first results of sea ice detection using the data of Ka- and Ku-band radars at low incidence angles. A classification method based on an unsupervised K-means approach is applied to the arrays of the data for the Arctic and Antarctic regions. Comparison with Advanced Microwave Scanning Radiometer 2 (AMSR-2) data was performed, and the dependence of classification performance was evaluated for incidence angles from 0° to 18.15°. This paper evaluates the classification accuracy of sea ice detection based on Ku-band, Ka-band, and their combination. Preliminary results indicate that the classification based solely on Ku-band data achieves the best performance. [ABSTRACT FROM AUTHOR]

The fault detection time identified by relying on the over-voltage criterion of zero-sequence voltage often lags behind the actual occurrence time of ground faults, which may cause fault protection methods based on transient quantity principles to miss fault characteristics and lose their protection capability. To accurately estimate the time of occurrence of a single-phase-to-ground fault, this paper proposes a two-step dimensionality reduction algorithm for estimating the time of occurrence of a single-phase-to-ground fault in a distribution network. This algorithm constructs a filter based on Empirical Mode Decomposition (EMD) to establish a high-dimensional feature dataset based on the zero-sequence current of all feeders. After Principal Component Analysis and Hilbert Mapping Algorithm, the high-dimensional data are reduced to two dimensions to construct a two-dimensional feature dataset. The density-based clustering method is used to adaptively divide the data into two categories, fault data and non-fault data, so as to estimate the time of occurrence of the fault. The paper designs 11 sets of experiments including 7 common high-resistance grounding mediums to verify the accuracy of the fault time recognition of this algorithm. The accuracy of this algorithm is within 7.3 ms and it exhibits better detection performance compared to the threshold detection method. [ABSTRACT FROM AUTHOR]

The phenomenon of algal bloom seriously affects the function of the aquatic ecosystems, damages the landscape of urban river and lakes, and threatens the safety of water use. The introduction of a multi-attribute decision-making method avoids the shortcomings of traditional algal bloom management that relies on manual experience. However, the weight-calculation part of this method still receives the artificial influence of human factors, which reduces the accuracy and scientific rigor of the decision. This paper presents a group decision-making method based on information self-learning which makes decision weights automatically clustered and assigned. A general framework of decision-making management is constructed for the algal bloom management process. In the decision-making process, an improved density-based clustering algorithm is used to automatically cluster and rank the decision data in the form of the three-parameter interval number, and ultimately obtain the optimal management method that meets the management objectives. Finally, the method was applied at the monitoring station of Sanjiadian Reservoir in Beijing, China. Based on the treatment objectives and water quality monitoring data of the station, relevant experts were invited to evaluate the management solutions, and the information self-organizing algorithm of this paper was used to automatically rank the decision-making methods, and finally obtain the most suitable management method for the station. Comparison with the water quality data and treatment inputs after the previous man-made selection of treatment options, and discussion among experts, show that the decision-making method is feasible and effective, and contributes to the sustainable treatment of algal blooms. [ABSTRACT FROM AUTHOR]

Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree "MST", and meta-heuristic algorithms, including Genetic Algorithm "GA" and Particle Swarm Optimization "PSO". The Fast Path-based Clustering "FPC" algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and a new hybrid meta-heuristic algorithm in a few rounds of algorithm iterations. This algorithm can achieve the global optimal value, and the main clustering process of the algorithm has a computational complexity of O (k² × n). However, due to the complexity of the minimum distance algorithm, the total computational complexity is O (n²). Experimental results of FPC on synthetic datasets with arbitrary shapes demonstrate that the algorithm is resistant to noise and outliers and can correctly identify clusters of varying sizes and numbers. In addition, the FPC requires the number of clusters as the only parameter to perform the clustering process. A comparative analysis of FPC and other clustering algorithms in this domain indicates that FPC exhibits superior speed, stability, and performance. [ABSTRACT FROM AUTHOR]

Further reducing greenhouse gas and pollutant emissions from road vehicles is a major task for the automotive industry. Stricter regulations regarding emissions and fleet fuel consumption require the continuous development of new powertrains and methods. In particular, the combination of hybrid powertrains on the technical side and the focus on real driving emissions (RDE) on the legislative side pose significant challenges to the vehicle calibration process. Against this background, new test methods and environments are being investigated to counteract the high number of interactions between hybrid drive systems and quasi-infinite test conditions due to RDE. Complementary to new test environments, innovative methods for data analysis are needed that allow the exploitation of the complete potential of measurement data. The application of such a method in the field of emission calibration is presented in this paper. For this purpose, a clustering method (HDBSCAN) is applied to critical sequences from emission tests. Within this presentation, the clustering process is based on a single signal only. This paper shows how signals of various characteristics can be processed with dynamic time warping and generically structured with the clustering method used. Here, 959 single events are automatically categorized into 24 clusters. This provides a new basis for system evaluation, enabling the automatic identification, categorization, and prioritization of calibration weaknesses. Using twelve signals of different characteristics, the generic usability of the clustering method is demonstrated. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to defect detection system design focused on exact damaged areas demonstrated through visual data containing gear wheel images. The main advantage of the system is the capability to detect a wide range of patterns of defects occurring in datasets. The methodology is built on three processes that combine different approaches from unsupervised and supervised methods. The first step is a search for anomalies, which is performed by defining the correct areas on the controlled object by using the autoencoder approach. As a result, the differences between the original and autoencoder-generated images are obtained. These are divided into clusters using the clustering method (DBSCAN). Based on the clusters, the regions of interest are subsequently defined and classified using the pre-trained Xception network classifier. The main result is a system capable of focusing on exact defect areas using the sequence of unsupervised learning (autoencoder)–unsupervised learning (clustering)–supervised learning (classification) methods (U2S-CNN). The outcome with tested samples was 177 detected regions and 205 occurring damaged areas. There were 108 regions detected correctly, and 69 regions were labeled incorrectly. This paper describes a proof of concept for defect detection by highlighting exact defect areas. It can be thus an alternative to using detectors such as YOLO methods, reconstructors, autoencoders, transformers, etc. [ABSTRACT FROM AUTHOR]

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The stability of Wireless Sensor Networks (WSNs) is a crucial requirement in real-time applications such as military, defense, and other surveillance systems. Clustering in WSNs is one of the most predominant techniques, offering benefits such as minimizing communication time, optimizing energy utilization, lengthening the network lifespan, and ensuring network stability. Moreover, achieving the balance between energy consumption and maintaining network stability is significantly influenced by the cluster size. This research paper addresses the challenges associated with clustered-based routing and cluster formation paradigm, introducing a novel algorithm employing the principle of Artificial Neural Networks (ANN). In particular, the proposed algorithm integrates a hybrid strategy of Self Organizing Map (SOM) and K-mean clustering to form energy-efficient clusters, even ensuring the energy distribution evenly among them. The validity of the proposed algorithm has been confirmed through experimental analysis conducted using MATLAB. The simulation results demonstrate that the hybrid combination of SOM and K-means clustering is highly efficient in minimizing energy consumption and maintaining network stability. The findings also reveal an average of 80% stable network lifetime and achieve a packet reception ratio (PRR) of 98% which is much higher than two other protocols i.e. LEACH and MODLEACH protocols. [ABSTRACT FROM AUTHOR]

Many datasets including social media data and bibliographic data can be modeled as graphs. Clustering such graphs is able to provide useful insights into the structure of the data. To improve the quality of clustering, node attributes can be taken into account, resulting in attributed graphs. Existing attributed graph clustering methods generally consider attribute similarity and structural similarity separately. In this paper, we represent attributed graphs as star-schema heterogeneous graphs, where attributes are modeled as different types of graph nodes. This enables the use of personalized pagerank (PPR) as a unified distance measure that captures both structural and attribute similarities. We employ DBSCAN for clustering, and we update edge weights iteratively to balance the importance of different attributes. The rapidly growing volume of data nowadays challenges traditional clustering algorithms, and thus, a distributed method is required. Hence, we adopt a popular distributed graph computing system Blogel, based on which, we develop four exact and approximate approaches that enable efficient PPR score computation when edge weights are updated. To improve the effectiveness of the clustering, we propose a simple yet effective edge weight update strategy based on entropy. In addition, we present a game theory based method that enables trading efficiency for result quality. Extensive experiments on real-life datasets offer insights into the effectiveness and efficiency of our proposals. [ABSTRACT FROM AUTHOR]

Healthcare analytics provide many benefits in healthcare dashboard systems. Healthcare datasets majorly contains categorical attributes. This paper proposed an optimized clustering for healthcare dataset named harmony search based categorical clustering (HSCC). The existing k-modes clustering algorithm is one of the well-known categorical data-clustering algorithm. Since the k-modes algorithm produces local optimal clusters. Generally, researchers use genetic algorithm (GA) based clustering algorithms to converge locally optimal solutions to global optimal solutions. GA has some deficiencies such as premature convergence with low speed. In this paper, harmony search (HS) optimization algorithm used to optimize clustering results. The result shows the proposed HSCC algorithm produced global optimized solution, unbiased and matured results. HSCC produces 98% accuracy for dental and 71% for lung cancer dataset. While GACC produces 95% and 65% accuracy for dental dataset and lung cancer dataset. [ABSTRACT FROM AUTHOR]

As an extension of Fuzzy C-Means (FCM), Evidence C-Means (ECM) is proposed in the framework of Dempster–Shafer theory (DST) and has been applied to many fields. However, the objective function of ECM involves only the distortion between the object and the prototype, which relies heavily on the initial prototype. Therefore, ECM may encounter the problem of local optimization. To solve this problem, this paper introduces ECM with Particle Swarm Optimization (PSO) initialization to determine the initial clustering centroids, and proposes Particle Swarm Optimization-based Evidential C-Means (PSO-ECM), which reduces the influence of bad initial prototypes and improves the local optimality problem of ECM. PSO-ECM is compared with three other clustering algorithms in four experiments and with ECM on a noise-containing dataset. According to the experimental results, PSO-ECM performs well in terms of different clustering validity metrics compared with existing clustering algorithms, has high stability of clustering, and can effectively and stably cluster noise-containing datasets and accurately identify outlier points. [ABSTRACT FROM AUTHOR]

Money laundering identification (MLI) is a challenging task for financial AI research and application due to its massive transaction volume, label sparseness, and label bias. Most of the existing MLI methods focus on individual-level abnormal behavior while neglecting the community factor that money laundering is a collaborative group crime. Furthermore, the massive volume of transactions and the issue of label shifting also impede the application of supervised or semi-supervised models. To this end, this paper proposes an efficient community-oriented algorithm, namely SEGE, to identify money laundering based on structural entropy minimization (SEM) with graph embedding in an unsupervised approach. Experiments on both a private real-world money laundering network and a public synthetic dataset show that our SEGE algorithm derives prominent performance and outperforms the parameterized learning-based graph representation methods. Moreover, we find that there are pervasive sub-communities in the real-world money laundering network. Based on our local algorithm, we propose a real combat strategy against the money laundering group, in which when we have several scattered suspicious accounts in the transaction network, we are able to retrieve the whole money laundering group by the union of sub-communities with both high precision and high recall rates. [ABSTRACT FROM AUTHOR]

Recently, business sectors have focused on offering a wide variety of services through utilizing different modern technologies such as super apps in order to fulfill customers' needs and create a satisfactory user experience. Accordingly, studying the user experience has become one of the most popular trends in the research field due to its essential role in business prosperity and continuity. Thus, many researchers have dedicated their efforts to exploring and analyzing the user experience across social media, blogs, and websites, employing a variety of research methods such as machine learning to mine users' reviews. However, there are limited studies concentrated on analyzing super app users' experiences and specifically mining Arabic users' reviews. Therefore, this paper aims to analyze and discover the most important topics that affect the user experience in the super app environment by mining Arabic business sector users' reviews in Saudi Arabia using biterm topic modeling, CAMeL sentiment analyzer, and doc2vec with k-means clustering. We explore users' feelings regarding the extracted topics in order to identify the weak aspects to improve and the strong aspects to enhance, which will promote a satisfactory user experience. Hence, this paper proposes an Arabic text annotation framework to help the business sector in Saudi Arabia to determine the important topics with negative and positive impacts on users' experience. The proposed framework uses two approaches: topic modeling with sentiment analysis and topic modeling with clustering. As a result, the proposed framework reveals four important topics: delivery and payment, customer service and updates, prices, and application. The retrieved topics are thoroughly studied, and the findings show that, in most topics, negative comments outweigh positive comments. These results are provided with general analysis and recommendations to help the business sector to improve its level of services. [ABSTRACT FROM AUTHOR]

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

Kidney segmentation from abdominal computed tomography (CT) images is essential for computer-aided kidney diagnosis, pathology detection, and surgical planning. This paper introduces a kidney segmentation method for clinical contrast-enhanced CT images. First, it begins with shape-based preprocessing to remove the spine and ribs. Second, a novel clustering algorithm and an initial kidney selection strategy are utilized to locate the initial slices and contours. Finally, an adaptive narrow-band approach based on active contours is developed, followed by a clustering postprocessing to address issues with concave parts. Experimental results demonstrate the high segmentation performance of the proposed method, achieving a Dice Similarity Coefficient of 97.4 ± 1.0% and an Average Symmetric Surface Distance of 0.5 ± 0.2 mm across twenty sequences. Notably, this method eliminates the need for manually setting initial contours and can handle intensity inhomogeneity and varying kidney shapes without extensive training or statistical modeling. [ABSTRACT FROM AUTHOR]

The purpose of clustering is to partition data similar with each other into a same group and partition data dissimilar with each other into different groups. However, in most existing fuzzy clustering approaches, the membership degrees of an individual belonging to different clusters are relied on the different distances between the individual and different cluster centroids, the similarity between the individual and data in different clusters are ignored, besides, the outliers cannot be distinguished effectively. For improving the clustering performance and addressing the problems in most existing fuzzy clustering approaches, based on the concept that data close to each other should be grouped together and be assigned to a same cluster, in this paper, we present a novel efficient fuzzy clustering approach, in which the adaptive local neighborhood information of each data referring to different clusters is taken into consideration; an entirely new idea that the membership degree values of an individual referring to different clusters should not only depend on the distances between the individual and different cluster centers, but also rely on the distances between the individual and several nearest neighbor data in different clusters is put forward; a new scheme to search for the outliers is presented, a method for identifying the different importance of different features is introduced. Experiments on synthetic and publicly real-world datasets were conducted, the clustering results show that the approach put forward can yield consistently satisfying clustering performance and has significant advantages. [ABSTRACT FROM AUTHOR]

Clustering algorithms play a crucial role in early warning cybersecurity systems. They allow for the detection of new attack patterns and anomalies and enhance system performance. This paper discusses the problem of clustering data collected by a distributed system of network honeypots. In the proposed approach, when a network flow matches an attack signature, an appropriate label is assigned to it. This enables the use of semi-supervised learning algorithms and improves the quality of clustering results. The article compares the results of learning algorithms conducted with and without partial supervision, particularly non-negative matrix factorization and semi-supervised non-negative matrix factorization. Our results confirm the positive impact of labeling a portion of flows on the quality of clustering. [ABSTRACT FROM AUTHOR]

Background: Using next-generation sequencing technologies, scientists can sequence complex microbial communities directly from the environment. Significant insights into the structure, diversity, and ecology of microbial communities have resulted from the study of metagenomics. The assembly of reads into longer contigs, which are then binned into groups of contigs that correspond to different species in the metagenomic sample, is a crucial step in the analysis of metagenomics. It is necessary to organize these contigs into operational taxonomic units (OTUs) for further taxonomic profiling and functional analysis. For binning, which is synonymous with the clustering of OTUs, the tetra-nucleotide frequency (TNF) is typically utilized as a compositional feature for each OTU. Results: In this paper, we present AFIT, a new l-mer statistic vector for each contig, and AFITBin, a novel method for metagenomic binning based on AFIT and a matrix factorization method. To evaluate the performance of the AFIT vector, the t-SNE algorithm is used to compare species clustering based on AFIT and TNF information. In addition, the efficacy of AFITBin is demonstrated on both simulated and real datasets in comparison to state-of-the-art binning methods such as MetaBAT 2, MaxBin 2.0, CONCOT, MetaCon, SolidBin, BusyBee Web, and MetaBinner. To further analyze the performance of the purposed AFIT vector, we compare the barcodes of the AFIT vector and the TNF vector. Conclusion: The results demonstrate that AFITBin shows superior performance in taxonomic identification compared to existing methods, leveraging the AFIT vector for improved results in metagenomic binning. This approach holds promise for advancing the analysis of metagenomic data, providing more reliable insights into microbial community composition and function. Availability: A python package is available at: https://github.com/SayehSobhani/AFITBin. [ABSTRACT FROM AUTHOR]