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In this study, we explore spectral heterogeneity within plant canopies, a characteristic often observed in stressed plants where certain leaves or intra-leaf regions exhibit stress symptoms while others remain unaffected. Considering this variability in spectral signatures holds promise for enhancing remote sensing methodologies aimed at plant stress detection. Typically, remote sensing techniques analyze the plant as a whole, potentially overlooking stress-related spectral signatures due to the inclusion of unaffected pixels. We used a clustering-based technique, which incorporates semi-supervised learning elements for tuning hyper-parameters, to differentiate spectral patterns associated with and unique to pixels from broomrape-infected (Orobanche spp. and Phelipanche spp.) carrots from unrelated patterns. Ground-based hyperspectral (400–1000 nm) images of broomrape-infected and non-infected carrot canopies were used in an agglomerative clustering procedure followed by spectral angle mapper (SAM) analysis to identify a spectral endmember indicative of broomrape infection symptoms. Pixels from this cluster constituted an average of 8.5–11.5 % from the canopies of infected plants. Subsequently, we: (a) examined the relationship between carrot leaf mineral content and the percentage of symptomatic pixels to explore stress-induced alterations creating the unique spectral signatures of infected plants; and (b) utilized the inverse mode of PROSPECT, a radiative transfer model (RTM), to derive primary plant traits from the distinct spectral data of each cluster. We found that deficits in two macro elements, phosphorous and potassium, along with two pigments, chlorophyll and carotenoid, were correlated with the symptomatic cluster in infected plants. The methodology presented in this study paves the way for further research into broomrape detection in various crop species, as well as other plant stressors.

Let { A n } n be a sequence of square matrices such that size (A n) = d n → ∞ as n → ∞. We say that { A n } n has an asymptotic spectral distribution described by a Lebesgue measurable function f : D ⊂ R k → C if, for every continuous function F : C → C with bounded support, lim n → ∞ ⁡ 1 d n ∑ i = 1 d n F (λ i (A n)) = 1 μ k (D) ∫ D F (f (x)) d x , where μ k is the Lebesgue measure in R k and λ 1 (A n) , ... , λ d n (A n) are the eigenvalues of A n. In the last decades, the asymptotic spectral distribution of increasing size matrices has become a subject of investigation by several authors. Special attention has been devoted to matrices A n arising from the discretization of differential equations, whose size d n diverges to ∞ along with the mesh-fineness parameter n. However, despite the popularity that the topic has reached nowadays, the role of the so-called test functions F appearing in the above limit relation has been inexplicably neglected so far. In particular, a natural question such as "Which is the largest set of test functions F for which the above limit relation is satisfied?" is still unanswered. In the present paper, we provide a definitive answer to this question by identifying the largest set of test functions F for which the above limit relation is satisfied. We also present some applications of this result to the analysis of spectral clustering, including new asymptotic estimates on the number of outliers. A special attention is devoted to the so-called "inner outliers" of Hermitian block Toeplitz matrices. We conclude the paper with an interpretation of the main result in the context of the vague convergence of probability measures. [ABSTRACT FROM AUTHOR]

Community detection is a crucial aspect in network analysis. Real-world networks are often enriched with attributes providing extensive information for nodes beyond mere topology. Integrating these nodal attributes into community detection for attributed networks poses notable challenges and remains an active research field. In this paper, we propose a novel method that incorporates structural information into fused attributes. This is achieved by defining a fusion similarity between nodes, which is a convex combination of topology similarity, pairwise attribute similarity, and attribute similarity with their immediate neighbors. One advantage of the proposed method is its flexibility in identifying communities in disassortative networks, where nodes exhibit more connections between different groups than within their own group. We employ an iterative spectral clustering technique to discover communities and assess the influence of various attributes within these communities. Our experimental results validate the effectiveness of this approach, demonstrating its utility in leveraging node attributes in diverse simulated and real-world network datasets. [ABSTRACT FROM AUTHOR]

The growing complexity of biological data has spurred the development of innovative computational techniques to extract meaningful information and uncover hidden patterns within vast datasets. Biological networks, such as gene regulatory networks and protein-protein interaction networks, hold critical insights into biological features' connections and functions. Integrating and analyzing high-dimensional data, particularly in gene expression studies, stands prominent among the challenges in deciphering these networks. Clustering methods play a crucial role in addressing these challenges, with spectral clustering emerging as a potent unsupervised technique considering intrinsic geometric structures. However, spectral clustering's user-defined cluster number can lead to inconsistent and sometimes orthogonal clustering regimes. We propose the Multi-layer Bundling (MLB) method to address this limitation, combining multiple prominent clustering regimes to offer a comprehensive data view. We call the outcome clusters "bundles". This approach refines clustering outcomes, unravels hierarchical organization, and identifies bridge elements mediating communication between network components. By layering clustering results, MLB provides a global-to-local view of biological feature clusters enabling insights into intricate biological systems. Furthermore, the method enhances bundle network predictions by integrating the bundle co-cluster matrix with the affinity matrix. The versatility of MLB extends beyond biological networks, making it applicable to various domains where understanding complex relationships and patterns is needed. [ABSTRACT FROM AUTHOR]

Imbalanced data poses a significant challenge in machine learning, as conventional classification algorithms often prioritize majority class samples, while accurately classifying minority class samples is more crucial. The synthetic minority oversampling technique (SMOTE) represents one of the most renowned methods for handling imbalanced data. However, both SMOTE and its variants have limitations due to their insufficient consideration of data distribution, leading to the generation of incorrect and unnecessary samples. This paper, therefore, introduces a novel oversampling algorithm called data distribution and spectral clustering-based SMOTE (DDSC-SMOTE). This algorithm addresses the shortcomings of SMOTE by introducing three innovative data distribution-based improvement strategies: adaptive allocation of synthetic sample quantities strategy, seed sample adaptive selection strategy, and synthetic sample improvement strategy. First, we use the k-nearest neighbor sample labels and the local outlier factor algorithm to remove noisy and outlier samples. Next, we leverage spectral clustering to identify clusters within the minority class and propose a dual-weight factor that considers inter-cluster and intra-cluster distances to allocate the number of synthetic samples effectively, addressing interclass and intraclass imbalances. Furthermore, we introduce a relative position weight coefficient to determine the probability of selecting seed samples within the subcluster, ensuring that important minority samples have higher chances of being sampled. Finally, we improve the SMOTE sample synthesis formula for safer generation. Extensive comparisons on real datasets from the UCI repository demonstrate that DDSC-SMOTE outperforms seven state-of-the-art oversampling algorithms significantly in terms of G-mean and F1-score, presenting a data distribution-focused solution for addressing imbalanced data challenges. [ABSTRACT FROM AUTHOR]

In recent decades, multi-view clustering has received a lot of attention. The majority of previous research has assumed that all instances have complete views or at least one view that includes all instances. However, the incomplete multi-view clustering issue arises because real-world data frequently lack instances in each view. We propose a novel anchor-based sparse subspace incomplete multi-view clustering solution to this issue. Through a unified sparse subspace learning framework, the proposed method learns inter-view anchor-to-anchor and intra-view anchor-to-incomplete affinities and fuses them into a consensus sparse anchor graph, which yields a unified clustering result. Our method outperforms other incomplete multi-view clustering methods in three important ways: (1) it uses a small number of hyperparameters to learn a sparse consensus graph from the data; (2) Because of the anchor-based graph construction, it can process large datasets; (3) It is naturally capable of handling both negative entries and multiple views. Last but not least, extensive experiments show that the proposed method is effective, supporting the claim that it consistently outperforms current clustering methods. [ABSTRACT FROM AUTHOR]

Batteries are a fundamental storage component due to its various applications in mobility, renewable energies and consumer electronics among others. Regardless of the battery typology, one key variable from a user's perspective is the remaining energy in the battery. It is usually presented as the percentage of remaining energy compared to the total energy that can be stored and is labeled State Of Charge (SOC). This work addresses the development of a hybrid model based on a Lithium Iron Phosphate (LiFePO4) power cell, due to its broad implementation. The proposed model calculates the SOC, by means of voltage and electric current as inputs and the latter as the output. Therefore, four models based on k-Means, Agglomerative Clustering, Gaussian Mixture and Spectral Clustering techniques have been tested in order to obtain an optimal solution. [ABSTRACT FROM AUTHOR]

Spectral clustering techniques depend on the eigenstructure of a similarity matrix to assign data points to clusters, so that points within the same cluster exhibit high similarity and are compared to those in different clusters. This work aimed to develop a spectral method that could be compared to clustering algorithms that represent the current state of the art. This investigation conceived a novel spectral clustering method, as well as five policies that guide its execution, based on spectral graph theory and embodying hierarchical clustering principles. Computational experiments comparing the proposed method with six state-of-the-art algorithms were undertaken in this study to evaluate the clustering methods under scrutiny. The assessment was performed using two evaluation metrics, specifically the adjusted Rand index, and modularity. The obtained results furnish compelling evidence, indicating that the proposed method is competitive and possesses distinctive properties compared to those elucidated in the existing literature. This suggests that our approach stands as a viable alternative, offering a robust choice within the spectrum of available same-purpose tools. [ABSTRACT FROM AUTHOR]

Cluster analysis, a pivotal step in single-cell sequencing data analysis, presents substantial opportunities to effectively unveil the molecular mechanisms underlying cellular heterogeneity and intercellular phenotypic variations. However, the inherent imperfections arise as different clustering algorithms yield diverse estimates of cluster numbers and cluster assignments. This study introduces Single Cell Consistent Clustering based on Spectral Matrix Decomposition (SCSMD), a comprehensive clustering approach that integrates the strengths of multiple methods to determine the optimal clustering scheme. Testing the performance of SCSMD across different distances and employing the bespoke evaluation metric, the methodological selection undergoes validation to ensure the optimal efficacy of the SCSMD. A consistent clustering test is conducted on 15 authentic scRNA-seq datasets. The application of SCSMD to human embryonic stem cell scRNA-seq data successfully identifies known cell types and delineates their developmental trajectories. Similarly, when applied to glioblastoma cells, SCSMD accurately detects pre-existing cell types and provides finer sub-division within one of the original clusters. The results affirm the robust performance of our SCSMD method in terms of both the number of clusters and cluster assignments. Moreover, we have broadened the application scope of SCSMD to encompass larger datasets, thereby furnishing additional evidence of its superiority. The findings suggest that SCSMD is poised for application to additional scRNA-seq datasets and for further downstream analyses. [ABSTRACT FROM AUTHOR]

It is important to identify the discriminative features for high dimensional clustering. However, due to the lack of cluster labels, the regularization methods developed for supervised feature selection can not be directly applied. To learn the pseudo labels and select the discriminative features simultaneously, we propose a new unsupervised feature selection method, named GlObal and Local information combined Feature Selection (GOLFS), for high dimensional clustering problems. The GOLFS algorithm combines both local geometric structure via manifold learning and global correlation structure of samples via regularized self-representation to select the discriminative features. The combination improves the accuracy of both feature selection and clustering by exploiting more comprehensive information. In addition, an iterative algorithm is proposed to solve the optimization problem and the convergency is proved. Simulations and two real data applications demonstrate the excellent finite-sample performance of GOLFS on both feature selection and clustering. [ABSTRACT FROM AUTHOR]

Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: The use of machine learning is exploding in all areas of healthcare, including the diagnosis and treatment of heart failure. Supervised machine learning can help predict the onset of heart failure, establish the diagnosis, and even predict decompensations. Conversely, unsupervised machine learning is chiefly used for phenotyping of the heart failure population. Several studies have identified distinctive groups of heart failure patients, but the widespread clinical implementation is still lacking. Aims: Our study aims to identify groups with similar characteristics among patients cared for HFrEF at the City Major Heart and Vascular Clinic of Semmelweis University using unsupervised machine learning and to describe the characteristic features of the resulting groups. We then examine the differences in outcome between the resulting groups. Methods: data from outpatients with reduced left ventricular ejection fraction heart failure were collected in a prospective registry. A total of 27 parameters included anamnestic data, laboratory tests, echocardiographic parameters and EQ5D quality of life questionnaire scores. The composite of hospitalization for heart failure and all-cause mortality was considered as the endpoint of the study. Spectral clustering was used to divide the population into three groups. The groups were plotted spatially using principal component analysis. Finally, we compared the groups in terms of parameters and endpoint occurrence. Results: Three characteristic groups were identified in the analysis of 259 patients. The first group consisted of 89 patients with ischemic etiology, more complaining, renal failure, and requiring duck diuretic therapy. The second group of 99 patients consisted of predominantly younger patients with atrial fibrillation, non-ischemic cardiomyopathy, dilated left ventricle, and a lower ejection fraction, almost exclusively on ARNI therapy. The third group of 71 patients included patients with the best ejection fraction, frequently taking ACE inhibitors and MRAs, and not requiring loop diuretics. Group 1 had significantly worse prognosis than group 2 (p=0.013) with a trend to worse prognosis compared to group 3. Conclusion: Our study identified three groups of patients with different characteristics based on a prospectively managed HFrEF registry. After validation on a larger patient cohort, our data may provide a basis for developing targeted treatment strategies.

With the country vigorously promoting structural reform on the supply side of energy, the installed capacity of new energy sources has been rising and competition in the power market has become increasingly fierce. On the other hand, the complexity and volatility of the global coal market has led to a rise in the cost of power generation enterprises using coal as their energy source. Coal heat value is one of the most important evaluation criteria for coal quality and is also the most important basis for coal procurement. Accurate prediction of coal heat value can effectively control power plant operation and procurement costs. In order to achieve efficient prediction of the heat value of coal, the Pearson coefficients were used to select the characteristics of the variables of interest, the DBSCAN algorithm was used to de-noise 1733 assay data of a power plant's own coal plant in the past two years, and spectral clustering (SC) analysis was performed on the de-noised data. The classified subsample sets were then used to build prediction models using the extreme gradient boosting (XGBoost) algorithm and compared with Ordinary Least Squares (OLS) and support vector machines (SVM) models. The performance of the models was compared with that of OLS and SVM. The results show that the accuracy of the XGBoost-based coal-fired heat value prediction model for power stations is significantly better than that of the other algorithms, and the generalization ability is stronger. The prediction model can further improve the refinement level of the model and provide a reliable and efficient method for coal-fired power station heat value prediction.

Understanding daily water consumption patterns is crucial for efficient management and distribution of water resources, as well as for promoting energy conservation and achieving carbon peaking and neutrality targets. It compares performance of three clustering algorithms, K-means Clustering (KC), Agglomerative Hierarchical Clustering (AHC), and Spectral Clustering (SC), using Silhouette Coefficient Index (SCI) and Calinski–Harabasz Index (CHI) as evaluation metrics. We conducted a case study using original hourly flow series of a water distribution division. It aims to identify typical daily water consumption patterns and explore factors that influence them. Findings are as follows: (1) among the three algorithms, KC demonstrates the best, with SCI of 0.6315, 0.5922, and 0.6272, and CHI of 305.9207, 274.1120, and 302.4738 for KC, AHC, and SC, respectively. (2) KC successfully identifies three distinct typical daily water consumption patterns. (3) Results indicate a significant impact of seasons on daily water consumption patterns. (4) Conversely, weekdays and holidays have minimal effect on daily water consumption patterns. It highlights the importance of comprehending daily water consumption patterns and underscores the effectiveness of KC in identifying such patterns. Furthermore, it emphasizes the significant influence of seasons while revealing limited impact of weekdays and holidays on daily water consumption patterns. HIGHLIGHTS K-means Clustering performs the best among the three clustering algorithms.; Three typical daily water consumption patterns were identified in the case study.; Season was found to be a significant influencing factor for the three patterns.;

Abstract There are a series of challenges in microgrid transactions, and blockchain technology holds the promise of addressing these challenges. However, with the increasing number of users in microgrid transactions, existing blockchain systems may struggle to meet the growing demands for transactions. Therefore, this paper proposes an efficient and secure blockchain consensus algorithm designed to meet the demands of large-scale microgrid electricity transactions. The algorithm begins by utilizing a Spectral clustering algorithm to partition the blockchain network into different lower-level consensus set based on the transaction characteristics of nodes. Subsequently, a dual-layer consensus process is employed to enhance the efficiency of consensus. Additionally, we have designed a secure consensus set leader election strategy to promptly identify leaders with excellent performance. Finally, we have introduced an authentication method that combines zero-knowledge proofs and key sharing to further mitigate the risk of malicious nodes participating in the consensus. Theoretical analysis indicates that our proposed consensus algorithm, incorporating multiple layers of security measures, effectively withstands blockchain attacks such as denial of service. Simulation experiment results demonstrate that our algorithm outperforms similar blockchain algorithms significantly in terms of communication overhead, consensus latency, and throughput.

Abstract This paper introduces a novel model for spectral clustering to solve the problem of poor connectivity among points within the same cluster as this can negatively impact the performance of spectral clustering. The proposed method leverages both sparsity and connectivity properties within each cluster to find a consensus similarity matrix. More precisely, the proposed approach considers paths of varying lengths in the graph, computing a similarity matrix for each path, and generating a cluster for each path. By combining these clusters using multi-view spectral clustering, the method produces clusters of good quality and robustness when there are outliers and noise. The extracted multiple independent views from different paths in the graph are integrated into a consensus graph. The performance of the proposed method is evaluated on various benchmark datasets and compared to state-of-the-art techniques.

Spectral clustering, as an algorithm based on graph theory and spectral theory, has shown excellent performance in the classification tasks of hyperspectral images in recent years. Although better results have been achieved, some challenges still exist. The inclusion of a priori information can increase the performance of spectral clustering algorithms; however, in practice, it is often unable to meet the demand for a priori information; at the same time, spectral clustering for the quality of the similarity matrix is demanding, and some of the current algorithms are to improve the similarity matrix from the aspect of data planning, and ultimately for the label feature matrices are diluted. In response to the above problems, this paper proposes a self-supervised spectral clustering with spectral embedding (SESSC). The algorithm obtains a low-dimensional representation of the data through spectral embedding, which can simplify clustering while preserving feature information; then uses the similarity matrix about the data and the guidance of the sample constraint information to obtain a new similarity matrix in order to further refine the structural graph, and the result can feed back and optimize the label feature matrix and the low-dimensional representation of the data. Additionally, we introduce a fractional theory in the update of the sample variable matrix, which assures the integrity and validity of the information in the update. Experimental results show that the proposed algorithm has better performance in hyperspectral image classification than existing spectral clustering algorithms. [ABSTRACT FROM AUTHOR]

Frequent and severe icing on transmission lines poses a serious threat to the stability and safe operation of the power system. Meteorological data, inherently stochastic and uncertain, requires effective preprocessing and feature extraction to ensure accurate and efficient prediction of transmission line icing thickness. We address this challenge by leveraging the meteorological features of icing phenomena and propose a novel feature preprocessing method that integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and spectral clustering. This method effectively preprocesses raw time series data, extracts key features, and constructs a similarity matrix and feature vector. The resulting feature vector serves as a new data representation, facilitating cluster analysis to isolate meteorological and icing-related features specific to transmission lines. Subsequently, we introduce an enhanced Transformer model for predicting transmission line icing thickness. The proposed model leverages the extracted meteorological and icing features by independently embedding variable tokens for each input feature. This approach improves the model's prediction accuracy under multiple feature inputs, leading to more effective learning. The experimental results demonstrate that the performance of the proposed prediction algorithm is better than the three baseline algorithms (hybrid CEEMDAN and LSTM (CEEMDAN-LSTM), hybrid CEEMDAN, spectral clustering, and LSTM (CEEMDAN-SP-LSTM), and hybrid CEEMDAN, spectral clustering, and Transformer (CEEMDAN-SP-Transformer)) under multiple feature inputs and different parameter settings. [ABSTRACT FROM AUTHOR]

Geographic flow clustering analysis can effectively reveal human behavioral patterns in movement. Traditional methods for studying human movement patterns are mostly based on first-order quantity analyses of point data, such as hotspots, density or clustering. Currently, relatively few second-order spatial analysis methods based on geographic flows exist. Thus, we developed a new geographic flow method based on spectral clustering and applied it to trajectory data analysis. This article uses the bike-sharing trajectories data in Shanghai in August 2016, spectral clustering analysis was conducted on the group flow patterns before, during and after rainfall, on weekdays and weekends and in the morning and evening peak. Spectral clustering was verified to exhibit better clustering effect by comparing the clustering indices of different clustering methods. This study enriches the analysis method of geographical flows, and the human mobility patterns revealed by its analysis can provide references for formulating urban green travel policies. [ABSTRACT FROM AUTHOR]

Generally, the classical problem of gradual pattern mining involves generating pattern candidates and determining the number of concordant object pairs associated with them. Given a numeric data set with n objects and m features, each feature yields two gradual items. Gradual pattern candidates can be formed by combining different sets of gradual items. In fact, a gradual pattern is composed of gradual items with similar concordant object pairs. However, computing the object pairs for each item has a complexity that is approximately quadratic in terms of the number of objects. As the main contribution of this paper, we propose finding gradual patterns by clustering gradual items based on their similarity in object pairs. First, we project the object pairs of each gradual item onto an n-dimensional subspace, thus reducing the complexity of computing object pairs from a quadratic function to a linear function. Second, we group gradual items into r clusters based on the similarity of object pairs in the n-dimensional subspace. As part of our experiments, we evaluated our approach using a variety of clustering algorithms. We found that the best clustering algorithms (across all the data sets we used) achieved precision scores above 55%, recall scores close to 100%, and F1 scores above 71%. [ABSTRACT FROM AUTHOR]

Psychological well-being was drastically affected due to changes in the social environment brought on by the COVID-19 pandemic. This study investigates how the COVID-19 pandemic affected individuals with anxiety disorders using narratives published on social media. Mid-pandemic and pre-pandemic datasets are collected and compiled from post titles published in the "r/Anxiety" subreddit, an internet community for people suffering from anxiety disorders. Then, a topic modelling pipeline based on clustering sentence embeddings in combination with sentiment analysis is applied to uncover trends in user narratives with the associated sentiment. In this study, three clustering algorithms, namely the Gaussian mixture model, spectral clustering, and k-means, were evaluated for their performance in clustering sentence embeddings using an internal evaluation method. The clusters formed reflected symptoms and types of anxiety disorders, demonstrating that unsupervised machine learning techniques, in particular topic modelling, can be used to detect mental health issues in social media data. [ABSTRACT FROM AUTHOR]

With the development of network technology, information networks have become one of the most important means for people to understand society. As the scale of information networks expands, the construction of network graphs and high-dimensional feature representation will become major factors affecting the performance of spectral clustering algorithms. To address this issue, in this paper, we propose a spectral clustering algorithm based on similarity graphs and non-linear deep embedding, named S E G _ S C . This algorithm introduces a new spectral clustering model that explores the underlying structure of graphs through sparse similarity graphs and deep graph representation learning, thereby enhancing graph clustering performance. Experimental analysis with multiple types of real datasets shows that the performance of this model surpasses several advanced benchmark algorithms and performs well in clustering on medium- to large-scale information networks. [ABSTRACT FROM AUTHOR]

Subspace clustering algorithms have demonstrated remarkable success across diverse fields, including object segmentation, gene clustering, and recommendation systems. However, they often face challenges, such as omitting cluster information and the neglect of higher-order neighbor relationships within the data. To address these issues, a novel subspace clustering method named Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering is proposed. This method seamlessly embeds Markov transition probability information into the self-expression, leveraging a fine-grained neighbor matrix to uncover latent data structures. This matrix preserves crucial high-order local information and complementary details, ensuring a comprehensive understanding of the data. To effectively handle complex nonlinear relationships, the method learns the underlying manifold structure from a cross-order local neighbor graph. Additionally, connectivity constraints are applied to the affinity matrix, enhancing the group structure and further improving the clustering performance. Extensive experiments demonstrate the superiority of this novel method over baseline approaches, validating its effectiveness and practical utility. [ABSTRACT FROM AUTHOR]

By a thorough performance comparison, we compare the recently proposed, operator-based linear clustering process on a network with classical, existing clustering algorithms. The linear clustering process produces clusters or partitions based on the eigenstructure of a linear operator on a graph that replaces nodes to 'more natural' positions by attractive and repulsive forces. Synthetic benchmarks, along with real-world networks possessing or lacking a known community structure, are considered. Our comparative analysis demonstrates that our linear clustering process generates superior partitions compared to the algorithms assessed in most instances, while of comparable computational complexity with the simplest existing clustering algorithms. [ABSTRACT FROM AUTHOR]

The pressing need for a transition towards a more sustainable economy has given rise to sustainability-oriented innovations (SOIs). The development of SOIs involves a wide range of emerging technologies, some of which are highly uncertain and may have the potential to transform an existing industry. Thus, the identification and assessment of emerging technologies is pivotal for established companies to evaluate business opportunities as well as for researchers and policy makers to support the transition to a more sustainable economy. Therefore, we aim to contribute by offering a new approach that seeks to assess such emerging technologies that contribute to sustainability transition (i.e., sustainability-oriented technologies (SOTs)) by means of spectral cluster analysis based on the semantic similarities of scientific research articles in the field of precision agriculture (PA); a case example where multiple SOIs occur. Our analyses reveal that spectral cluster analysis is a suitable approach for assessing emerging SOTs. In addition, multi-perspective assessments based on, inter alia, related United Nations Sustainable Development Goals, Web of Science subject categories, strategic diagrams, and business models allows to render a holistic assessment of a scientific research area (in this case PA) and its emerging SOTs as well as their evolution into commercial SOIs. [ABSTRACT FROM AUTHOR]

In this digitally-driven culture, the need and demand for diarizing online meetings, classes, conferences, and medical diagnoses have increased a lot. Speaker Diarization, a sub-domain of Speaker Recognition has grown with the advent of neural networks in the last decade. Diarize generally refers to obtaining the duration of individual speakers in any event. Researchers have suggested various approaches for multiple-speaker diarization. However, it still suffers from a problem of various environmental noises, and non-speech sounds like laughter, murmuring, clapping, etc. in the datasets. Hence, this paper proposes an improved speaker diarization pipeline to deal with the noise present in a dataset having multiple speakers. This improved diarization pipeline uses Bi-directional Long Short-Term Memory (Bi-LSTM), based speech refinement pre-processing module, and Modified Spectral Clustering with Symmetrized Singular Value Decomposition (MSC-SSVD). MSC-SSVD is used to cater to the problem of spectral clustering in large datasets. The proposed diarization pipeline is evaluated using the publicly available VoxConverse dataset. The Diarization Error Rate (DER) obtained after experimentation are 37.2%, 37.1%, and 43.3% respectively for three batches of dataset under study. The results are also compared with the baseline system and significant change in DER by 6.1%, 4.7%, and 7% respectively for three batches is observed. [ABSTRACT FROM AUTHOR]

The process of identifying the best job candidates from different sets of resumes is a resource and time consuming process. The common issue in resume screening is the unavailability of annotated data to label the information obtained accurately. To tackle these challenges, this research undergoes feature extraction and feature clustering stages for resume screening and ranking. In this work, the hybrid deep learning (DL) based Pyramid Dilated Convolutional Neural Network with Bidirectional Gated Recurrent Unit (PDCNN-Bi-GRU) is introduced to extract the skill related features from the resumes. Moreover, the Conditional Random Field (CRF) layer is hybridized with the DL technique to generate skill-related information as the outcome. Then, a fuzzy matching module is contemplated to match the skill-related features with the job categories to enhance the accuracy performance. Finally, the candidate resumes of higher skills are clustered using the hybrid Spectral clustering with Hummingbird Optimization (SCHO) technique. The proposed method is tested with both public source and real-time datasets and is implemented in the PYTHON platform. The performances like accuracy, specificity, sensitivity, F-measure, kappa, time complexity, and Mean Square Error (MSE) are analyzed and compared with existing techniques. The proposed method obtains the accuracy of 99.3% and 99.83% for testing with both public source and real-time datasets, respectively. [ABSTRACT FROM AUTHOR]

As an exploratory data aَnalysis (EDA) process, spectral clustering (SC) reduces complex, multidimensional data sets to similar ones in rarer dimensions. Given the big challenges of high computational complexity and lack of accurate mapping in multidimensional data sets, it is essential to provide innovative solutions for SC. Against this background, the present study aims to propose a novel method based on extended Deep learning (DL), recruiting autoencoder (AE) ensembles. Here, the eagle strategy (ES) is further applied, rather than the anchor-based and landmark ones, to eliminate some complexities in SC and fill gaps in data training. As SC has a stochastic optimization structure, the ES may seem appropriate. The Fundamental Clustering and Projection Suite (FCPS) data set correspondingly represents that the proposed method has been able to surpass previous robust algorithms in terms of mean squared error (MSE), accuracy, adjusted random index (ARI), and normalized mutual information (NMI). [ABSTRACT FROM AUTHOR]

With the increased complexity of power systems stemming from the connection of high-proportion renewable energy sources, coupled with the escalating volatility and uncertainty, the key transmission sections that serve as indicators of the power grid's security status are also subject to frequent changes, posing challenges to grid monitoring. The search method for key transmission sections based on an improved spectral clustering algorithm is proposed in this paper. A branch weight model, considering the impact of node voltage and power flow factors, is initially established to comprehensively reflect the electrical connectivity between nodes. Subsequently, a weighted graph model is constructed based on spectral graph theory, and an improved spectral clustering algorithm is employed to partition the power grid. Finally, a safety risk indicator is utilized to identify whether the partitioned sections are key transmission sections. Results from case studies on the IEEE39-node system and actual power grid examples demonstrate that the proposed method accurately and effectively searches for all key transmission sections of the system and identifies their security risks. The application in real power grid scenarios validates its ability to screen out some previously unrecognized key transmission sections. [ABSTRACT FROM AUTHOR]

Users utilize Location-Based Social Networks (LBSNs) to check into diverse venues and share their experiences through ratings and comments. However, these platforms typically feature a considerably larger number of locations than users, resulting in a challenge known as insufficient historical data or user-location matrix sparsity. This sparsity arises because not all users can check into all available locations on a given LBSN, such as Yelp. To address this challenge, this paper proposes combining Spectral Clustering with a three-layered location recommendation model to develop a recommender system named LSC, applied to Yelp datasets. LSC leverages various information, including users' check-in data, demographics, location demographics, and users' friendship network data, to train the recommender system and generate recommendations. Evaluation of LSC's performance utilizes the Yelp dataset and several comparison metrics, such as accuracy, RMSE, and F1-score. The results demonstrate that our proposed algorithm delivers reliable and significant performance improvements across various evaluation metrics compared to competing algorithms. [ABSTRACT FROM AUTHOR]

In order to reduce the computational burden of large-scale data spectral clustering and further improve the clustering accuracy and robustness, the spectral clustering ensemble algorithm based on the three-order tensor for large-scale data was proposed. The sparse affinity sub-matrix was first constructed by the mixed representative nearest neighbor approximation method. The sparse affinity sub-matrix was then represented as a bipartite graph. The preliminary clustering results were obtained by Graph Segmentation. Finally, an unified clustering result was obtained by fusing multiple clustering results through the three-order tensor ensemble method. On the real datasets and the synthetic datasets, the proposed algorithm showed a better performance compared to the classical spectral clustering algorithm, the clustering ensemble algorithm, and the improved algorithms in recent years. [ABSTRACT FROM AUTHOR]

To tackle the problem of the brain storm optimization (BSO) algorithm's suboptimal capability for avoiding local optima, which contributes to its inadequate optimization precision, we developed a flock decision mutation approach that substantially enhances the efficacy of the BSO algorithm. Furthermore, to solve the problem of insufficient BSO algorithm population diversity, we introduced a strategy that utilizes the good point set to enhance the initial population's quality. Simultaneously, we substituted the K-means clustering approach with spectral clustering to improve the clustering accuracy of the algorithm. This work introduced an enhanced version of the brain storm optimization algorithm founded on a flock decision mutation strategy (FDIBSO). The improved algorithm was compared against contemporary leading algorithms through the CEC2018. The experimental section additionally employs the AUV intelligence evaluation as an application case. It addresses the combined weight model under various dimensional settings to substantiate the efficacy of the FDIBSO algorithm further. The findings indicate that FDIBSO surpasses BSO and other enhanced algorithms for addressing intricate optimization challenges. [ABSTRACT FROM AUTHOR]

We use Category Theory to construct a 'bridge' relating directed graphs with undirected graphs, such that the notion of direction is preserved. Specifically, we provide an isomorphism between the category of simple directed graphs and a category we call 'prime graphs category'; this has as objects labeled undirected bipartite graphs (which we call prime graphs), and as morphisms undirected graph morphisms that preserve the labeling (which we call prime graph morphisms). This theoretical bridge allows us to extend undirected graph techniques to directed graphs by converting the directed graphs into prime graphs. To give a proof of concept, we show that our construction preserves topological features when applied to the problems of network alignment and spectral graph clustering. [ABSTRACT FROM AUTHOR]

Background: Due to the scalability of deep learning technology, researchers have applied it to the non‐destructive testing of peach internal quality. In addition, the soluble solids content (SSC) is an important internal quality indicator that determines the quality of peaches. Peaches with high SSC have a sweeter taste and better texture, making them popular in the market. Therefore, SSC is an important indicator for measuring peach internal quality and making harvesting decisions. Results: This article presents the High Order Spatial Interaction Network (HOSINet), which combines the Position Attention Module (PAM) and Channel Attention Module (CAM). Additionally, a feature wavelength selection algorithm similar to the Group‐based Clustering Subspace Representation (GCSR‐C) is used to establish the Position and Channel Attention Module‐High Order Spatial Interaction (PC‐HOSI) model for peach SSC prediction. The accuracy of this model is compared with traditional machine learning and traditional deep learning models. Finally, the permutation algorithm is combined with deep learning models to visually evaluate the importance of feature wavelengths. Increasing the order of the PC‐HOSI model enhances its ability to learn spatial correlations in the dataset, thus improving its predictive performance. Conclusion: The optimal model, PC‐HOSI model, performed well with an order of 3 (PC‐HOSI‐3), with a root mean square error of 0.421 °Brix and a coefficient of determination of 0.864. Compared with traditional machine learning and deep learning algorithms, the coefficient of determination for the prediction set was improved by 0.07 and 0.39, respectively. The permutation algorithm also provided interpretability analysis for the predictions of the deep learning model, offering insights into the importance of spectral bands. These results contribute to the accurate prediction of SSC in peaches and support research on interpretability of neural network models for prediction. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

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