Our beliefs and opinions are shaped by others, making our social networks crucial in determining what we believe to be true. Sometimes this is for the good because our peers help us form a more accurate opinion. Sometimes it is for the worse because we are led astray. In this context, we address via agent-based computer simulations the extent to which patterns of connectivity within our social networks affect the likelihood that initially undecided agents in a network converge on a (...) true opinion following group deliberation. The model incorporates a fine-grained and realistic representation of belief and trust, and it allows agents to consult outside information sources. We study a wide range of network structures and provide a detailed statistical analysis concerning the exact contribution of various network metrics to collective competence. Our results highlight and explain the collective risks involved in an overly networked or partitioned society. Specifically, we find that 96% of the variation in collective competence across networks can be attributed to differences in amount of connectivity and clustering, which are negatively correlated with collective competence. A study of bandwagon or “group think” effects indicates that both connectivity and clustering increase the probability that the network, wholly or partly, locks into a false opinion. Our work is interestingly related to Gerhard Schurz’s work on meta-induction and can be seen as broadly addressing a practical limitation of his approach. (shrink)

The cluster account, one of the best attempts at art classification, is guilty of ahistoricism. While cluster theorists may be happy to limit themselves to accounting for what art is now rather than how the term was understood in the past, they cannot ignore the fact that people seem to apply different clusters when judging art from different times. This paper shows that while allowing for this kind of historical relativity may be necessary to save the account, doing so could (...) result in incorporating an essentially institutional component or making the theory extremely complex and virtually impossible to use. (shrink)

Noah Rosenberg et al.'s 2002 article “Genetic Structure of Human Populations” reported that multivariate genomic analysis of a large cell line panel yielded reproducible groupings (clusters) suggestive of individuals' geographical origins. The paper has been repeatedly cited as evidence that traditional notions of race have a biological basis, a claim its authors do not make. Critics of this misinterpretation have often suggested that it follows from interpreters' personal biases skewing the reception of an objective piece of scientific writing. I contend, (...) however, that the article itself to some degree facilitates this misrepresentation. I analyze in detail several verbal and visual features of the original article that may predispose aspects of its racial interpretation; and, tracing the arguments of one philosopher and one popular science writer, I show how these features are absorbed, transformed into arguments for a biological basis of race, and re-attributed to the original. The essay demonstrates how even slight ambiguities can enable the misappropriation of scientific writing, unintentionally undermining the authors' stated circumspection on the relationship between cluster and race. (shrink)

A recent concern in the debate on corporate social responsibility (CSR) in developing countries relates to the tension between demands for CSR compliance found in many global value chains (GVCs) and the search for locally appropriate responses to these pressures. In this context, an emerging and relatively understudied area of interest relates to small firm industrial clusters. Local clusters offer the potential for local joint action, and thus a basis for improving local compliance on CSR through collective monitoring and local (...) governance. This article explores the interrelationship between global governance, exercised through GVC ties, and local governance, via cluster institutions, in ensuring compliance with CSR pressures. It undertakes a comparative analysis of two leading export-oriented football manufacturing clusters in South Asia that have both faced common challenges on child labour. The article shows that both forms vertical and horizontal governance have played a part in shaping the response of the two clusters on child labour. Moreover, these two distinct forms of governance have also led to quite differentiated outcomes in terms of forms of work organization and child labour monitoring. This raises broader questions on how global CSR demands can locally be better embedded and the conditions under which football stitchers labour in these new work forms. (shrink)

A recent concern in the debate on corporate social responsibility (CSR) in developing countries relates to the tension between demands for CSR compliance found in many global value chains (GVCs) and the search for locally appropriate responses to these pressures. In this context, an emerging and relatively understudied area of interest relates to small firm industrial clusters. Local clusters offer the potential for local joint action, and thus a basis for improving local compliance on CSR through collective monitoring and local (...) governance. This article explores the interrelationship between global governance, exercised through GVC ties, and local governance, via cluster institutions, in ensuring compliance with CSR pressures. It undertakes a comparative analysis of two leading export-oriented football manufacturing clusters in South Asia that have both faced common challenges on child labour. The article shows that both forms vertical and horizontal governance have played a part in shaping the response of the two clusters on child labour. Moreover, these two distinct forms of governance have also led to quite differentiated outcomes in terms of forms of work organization and child labour monitoring. This raises broader questions on how global CSR demands can locally be better embedded and the conditions under which football stitchers labour in these new work forms. (shrink)

This paper introduces the Transfer Learning Particle Swarm for Clustering (TLPSC), a novel algorithm that integrates Particle Swarm Clustering (PSC) with Prototype-Based Transfer Learning to enhance clustering performance. By leveraging knowledge from a source domain, TLPSC improves cluster formation, particularly in sparse or noisy data scenarios. Experimental results show that TLPSC consistently outperforms traditional methods, including K-Means, Gaussian Mixture Models, and standard PSC, across multiple evaluation metrics, such as Normalized Mutual Information, Adjusted Rand Index, Silhouette Score, and (...) Davies–Bouldin Index. TLPSC effectively preserves the structure of the data and forms cohesive partitions, highlighting its robustness and applicability to complex clustering tasks. (shrink)

The appropriate understanding and fast processing of lengthy legal documents are computationally challenging problems. Designing efficient automatic summarization techniques can potentially be the key to deal with such issues. Extractive summarization is one of the most popular approaches for forming summaries out of such lengthy documents, via the process of summary-relevant sentence selection. An efficient application of this approach involves appropriate scoring of sentences, which helps in the identification of more informative and essential sentences from the document. In this work, (...) a novel sentence scoring approach DCESumm is proposed which consists of supervised sentence-level summary relevance prediction, as well as unsupervised clustering-based document-level score enhancement. Experimental results on two legal document summarization datasets, BillSum and Forum of Information Retrieval Evaluation (FIRE), reveal that the proposed approach can achieve significant improvements over the current state-of-the-art approaches. More specifically it achieves ROUGE metric F1-score improvements of (1−6)% and (6−12)% for the BillSum and FIRE test sets respectively. Such impressive summarization results suggest the usefulness of the proposed approach in finding the gist of a lengthy legal document, thereby providing crucial assistance to legal practitioners. (shrink)

This article provides a review of what we know, what we do not know, and what we need to know about the relationship between industrial clusters and corporate social responsibility in developing countries. In addition to the drivers of and barriers to the adoption of CSR initiatives, this study highlights key lessons learned from empirical studies of CSR initiatives that aimed to improve environmental management and work conditions and reduce poverty in local industrial districts. Academic work in this area remains (...) embryonic, lacking in empirical evidence about the effects of CSR interventions on the profitability on local enterprises, workers, and the environment. Nor do theoretical frameworks offer clear explanations of the institutionalization and effects of CSR in local industrial districts in the developing world. Other key limitations in this research stream include an excessive focus on export-oriented industrial clusters, the risk that CSR becomes a form of economic and cultural imperialism, and the potential for joint-action CSR initiatives in clusters of small and medium-sized enterprises to offer a new form of greenwashing. From this review, the authors develop a theoretical model to explain why CSR has not become institutionalized in many developing country clusters, which in turn suggests that the vast majority of industrial clusters in developing countries are likely to engage in socially irresponsible behavior. (shrink)

Existing fine-tuning data selection methods for large language models (LLMs) often struggle to balance two aspects: the evaluation of legal fine-tuning data quality and the maintenance of syntactic diversity. The assessment of data quality consists of two dimensions: legal content quality, encompassing logical rigor, legal basis traceability, fact coverage, and actionable guidance, and instructional complexity, which measures how challenging an instruction–response pair is for the model to learn. Syntactic diversity, in contrast, captures the variety of sentence structures and dependency patterns (...) within the dataset, reflecting how sentences differ in grammatical organization, for example through variations between active and passive voice, clause nesting, or modifier placement. To address these issues, this paper proposes LEAD (Legal Efficiency and Diversity), a fine-tuning data selection framework for legal LLMs that integrates dual-objective optimization with syntactic structure clustering. LEAD consists of two modules: the Legal Efficiency Training Score (LETS), which evaluates each sample’s legal quality, and Syntactic Diversity Clustering (SDC), which applies graph-kernel methods to ensure syntactic diversity. Experiments show that using only 5% of the original dataset selected by LEAD, the resulting model outperforms full-data training by 14.9% and achieves an average 12.3% improvement across legal benchmarks, demonstrating strong potential for efficient legal LLM fine-tuning. (shrink)

Languages use predicates and arguments to express events and event participants. In order to establish generalizations concerning the variety languages show regarding the strategies for discerning some arguments from the others, the concept of roles—and, particularly, macroroles, mesoroles, and microroles—associated with participants provides a widely studied starting point. In this article, the formal properties in the arguments of a set of 14 verb meanings in Spanish Sign Language have been analyzed. Arguments have been studied by considering their microroles, and a (...) quantitative method for measuring distances from a plurality of properties has been adopted. The novelty of this analysis is that it focuses on how arguments group in terms of these properties. Subsequently, some generalizations justifying why some verb meanings have a tendency to associate with certain forms of argument expression are highlighted in this study. (shrink)

The target paper shows how cultural adaptations to ecological problems can underpin “paradoxical” patterns of phenotypic variation. We argue: (1) Gendered social learning is a cultural adaptation to an ecological problem. (2) In evolutionarily novel environments, this adaptation generates arbitrary-gendered outcomes, leading to the paradoxical case of larger sex differences in more gender equal societies.

With the advent of big data, the performance of traditional recommendation algorithms is no longer enough to meet the demand. Most people do not leave too many comments and other data when using the application. In this case, the user data are too scattered and discrete, with obvious data sparsity problems. First, this paper describes the main ideas and methods used in current recommendation systems and summarizes the areas that need attention and consideration. Based on these algorithms and based on (...) the user history data information and music data information that can be found now, the paper aims to build a personalized music recommendation system based on directed tags, which can provide basic music services to users and push them personalized music recommendation lists. Then, the collaborative filtering algorithm based on tags is introduced. Usually this method uses discrete tags, and the user tags and music tags are juxtaposed and leveled with each other, which does not reflect the importance and ranking order relationship of each tag and does not reflect the cognitive sequence of users when they listen to and annotate music. In order to improve this problem and increase the accuracy of recommendations, the user-tag and music-tag data are correlated through the tag sequence of tag and music-tag data are correlated and modeled analytically, and feature directed graphs are constructed. (shrink)

Machine learning is a scientific discipline that can be divided into two main branches: supervised machine learning and unsupervised machine learning. In this paper, we aim to show just how simplicity matters in unsupervised contexts. This is important because unsupervised machine learning algorithms have barely received any attention in philosophy. Yet, there is a direct link between simplicity and truth in unsupervised contexts that we do not find in their supervised counterparts. This has thus far evaded philosophical discussions on simplicity.

In synthetic aperture radar image segmentation field, regional algorithms have shown great potential for image segmentation. The SAR images have a multiplicity of complex texture, which are difficult to be divided as a whole. Existing algorithm may cause mixed super-pixels with different labels due to speckle noise. This study presents the technique based on organization evolution algorithm to improve ISODATA in pixels. This approach effectively filters out the useless local information and successfully introduces the effective information. To verify the accuracy (...) of OEA-ISO data algorithm, the segmentation effect of this algorithm is tested on SAR image and compared with other techniques. The results demonstrate that the OEA-ISO data algorithm is 10.16% more accurate than the WIPFCM algorithm, 23% more accurate than the K-means algorithm, and 27.14% more accurate than the fuzzy C-means algorithm in the light-colored farmland category. It can be seen that the OEA-ISO data algorithm introduces the pixel block strategy, which successfully reduces the noise interference in the image, and the effect is more obvious when the image background is complex. (shrink)

In the framework of quantum field theory, one finds multiple load-bearing locality and causality conditions. One of the most important is the cluster decomposition principle, which requires that scattering experiments conducted at large spatial separation have statistically independent results. The principle grounds a number of features of quantum field theory, especially the structure of scattering theory. However, the statistical independence required by cluster decomposition is in tension with the long-range correlations characteristic of entangled states. In this paper, we argue that (...) cluster decomposition is best stated as a condition on the dynamics of a quantum field theory, not directly as a statistical independence condition. This redefinition avoids the tension with entanglement while better capturing the physical significance of cluster decomposition and the role it plays in the structure of quantum field theory. (shrink)

Clustering plays an important role in data mining, pattern recognition, and machine learning. Single-valued neutrosophic sets are useful means to describe and handle indeterminate and inconsistent information that fuzzy sets and intuitionistic fuzzy sets cannot describe and deal with. To cluster the data represented by single-valued neutrosophic information, this article proposes single-valued neutrosophic clustering methods based on similarity measures between SVNSs. First, we define a generalized distance measure between SVNSs and propose two distance-based similarity measures of SVNSs. Then, (...) we present a clustering algorithm based on the similarity measures of SVNSs to cluster single-valued neutrosophic data. Finally, an illustrative example is given to demonstrate the application and effectiveness of the developed clustering methods. (shrink)

Water quality prediction is the basis of water environmental planning, evaluation, and management. In this work, a novel intelligent prediction model based on the fuzzy wavelet neural network including the neural network, the fuzzy logic, the wavelet transform, and the genetic algorithm was proposed to simulate the nonlinearity of water quality parameters and water quality predictions. A self-adapted fuzzy c-means clustering was used to determine the number of fuzzy rules. A hybrid learning algorithm based on a genetic algorithm and (...) gradient descent algorithm was employed to optimize the network parameters. Comparisons were made between the proposed FWNN model and the fuzzy neural network, the wavelet neural network, and the neural network. The results indicate that the FWNN made effective use of the self-adaptability of NN, the uncertainty capacity of FL, and the partial analysis ability of WT, so it could handle the fluctuation and the nonseasonal time series data of water quality, while exhibiting higher estimation accuracy and better robustness and achieving better performances for predicting water quality with high determination coefficients R2 over 0.90. The FWNN is feasible and reliable for simulating and predicting water quality in river. (shrink)

EEG signal analysis is a powerful technique to decode the activities of the human brain. Emotion detection among individuals using EEG is often reported to classify people based on emotions. We questioned this observation and hypothesized that different people respond differently to emotional stimuli and have an intrinsic predisposition to respond. We designed experiments to study the responses of participants to various emotional stimuli in order to compare participant-wise categorization to emotion-wise categorization of the data. The experiments were conducted on (...) a homogeneous set of 20 participants by administering 9 short, one to two minute movie clips depicting different emotional content. The EEG signal data was recorded using the 128 channel high-density geodesic net. The data was filtered, segmented, converted to frequency domain and alpha, beta and theta ranges were extracted. Clustering was performed using a novel recursive-split and merge unsupervised algorithm. The data was analyzed through confusion matrices, plots and normalization techniques. It was found that the variation in emotive responses of a participant was significantly lower than the variation across participants. This resulted in more efficient participant-based clustering as compared to emotive stimuli-based clustering. We concluded that the emotive response is perhaps a signature of an individual with a characteristic pattern of EEG signals. Our findings on further experimentation will prove valuable for the progress of research in cognitive sciences, security and other related areas. (shrink)

ObjectiveTo compare techniques for computing clustering and switching scores in terms of agreement, correlation, and empirical value as predictors of incident cognitive impairment.MethodsWe transcribed animal and letter F fluency recordings on 640 cases of ICI and matched controls from a national epidemiological study, amending each transcription with word timings. We then calculated clustering and switching scores, as well as scores indexing speed of responses, using techniques described in the literature. We evaluated agreement among the techniques with Cohen’s κ (...) and calculated correlations among the scores. After fitting a base model with raw scores, repetitions, and intrusions, we fit a series of Bayesian logistic regression models adding either clustering and switching scores or speed scores, comparing the models in terms of several metrics. We partitioned the ICI cases into acute and progressive cases and repeated the regression analysis for each group.ResultsFor animal fluency, we found that models with speed scores derived using the slope difference algorithm achieved the best values of the Watanabe–Akaike Information Criterion, but with good net reclassification improvement only for the progressive group. For letter fluency, different models excelled for prediction of acute and progressive cases. For acute cases, NRI was best for speed scores derived from a network model, while for progressive cases, the best model used clustering and switching scores derived from the same network model. Combining variables from the best animal and letter F models led to marginal improvements in model fit and NRI only for the all-cases and acute-cases analyses.ConclusionSpeed scores improve a base model for predicting progressive cognitive impairment from animal fluency. Letter fluency scores may provide complementary information. (shrink)

In recent years, the field of historical semantics has experienced notable advancements. Despite the emergence of promising methodologies, a standardized procedure has not yet been established in the research of the history of concepts. A significant methodological challenge is the seamless and transparent integration of analyzing large datasets (distant reading) with the traditional workflows of conceptual historical research (close reading). Additionally, the effective capture of various word senses (polysemy) and tracking their change over time using computational methods remains a complex (...) task. This article presents the tool Sense Clustering over Time (SCoT), specifically designed to address these challenges in conceptual historical research. SCoT employs the method of Word Sense Induction (WSI) to facilitate the semi-automatic detection and visual representation of the historical semantics of conceptual words into sense clusters. Through an open-access web interface, users can: (1) analyze the diachronic development of sense clusters within extensive text corpora, (2) explore the linguistic contexts driving these changes, and (3) identify and compile relevant references in the text corpus for further study. The article explores the hermeneutic processes involved in the analysis of large historical text corpora utilizing a WSI approach and discusses epistemic challenges associated with using SCoT as a research tool in the digital history of concepts. (shrink)

The paper presents different clustering approaches in legal judgments from the Special Civil Court located at the Federal University of Santa Catarina. The subject is Consumer Law, specifically cases in which consumers claim moral and material compensation from airlines for service failures. To identify patterns from the dataset, we apply four types of clustering algorithms: Hierarchical and Lingo, K-means and Affinity Propagation. We evaluate the results based on the following criteria: entropy and purity; algorithm's ability in providing labels; (...) legal expert’s evaluation; and experimental complexity. The results demonstrate that the most advantageous approach is Hierarchical Clustering, since it has the best entropy and purity numbers, as well as the least difficulty for the expert to analyze the clusters, and the least experimental complexity. The main contribution of the paper is to show the advantages and disadvantages of each approach, especially to identify labels in unstructured and non-indexed legal texts. (shrink)

Based on the growing problem of heart diseases, their efficient diagnosis is of great importance to the modern world. Statistical inference is the tool that most physicians use for diagnosis, though in many cases it does not appear powerful enough. Clustering of patient instances allows finding out groups for which statistical models can be built more efficiently. However, the performance of such an approach depends on the features used as clustering attributes. In this paper, the methodology that consists (...) of combining unsupervised feature selection and grouping to improve the performance of statistical analysis is considered. We assume that the set of attributes used in clustering and statistical analysis phases should be different and not correlated. Thus, the method consisting of selecting reversed correlated features as attributes of cluster analysis is considered. The proposed methodology has been verified by experiments done on three real datasets of cardiovascular cases. The obtained effects have been evaluated regarding the number of detected dependencies between parameters. Experiment results showed the advantage of the presented approach compared to other feature selection methods and without using clustering to support statistical inference. (shrink)

Abnormal behavior detection of social security funds is a method to analyze large-scale data and find abnormal behavior. Although many methods based on spectral clustering have achieved many good results in the practical application of clustering, the research on the spectral clustering algorithm is still in the early stage of development. Many existing algorithms are very sensitive to clustering parameters, especially scale parameters, and need to manually input the number of clustering. Therefore, a density-sensitive similarity (...) measure is introduced in this paper, which is obtained by introducing new parameters to transform the Gaussian function. Under this metric, the distance between data points belonging to different classes will be effectively amplified, while the distance between data points belonging to the same class will be reduced, and finally, the distribution of data will be effectively clustered. At the same time, the idea of Eigen gap is introduced into the spectral clustering algorithm, and the verified gap sequence is constructed on the basis of Laplace matrix, so as to solve the problem of the number of initial clustering. The strong global search ability of artificial bee colony algorithm is used to make up for the shortcoming of spectral clustering algorithm that is easy to fall into local optimal. The experimental results show that the adaptive spectral clustering algorithm can better identify the initial clustering center, perform more effective clustering, and detect abnormal behavior more accurately. (shrink)

The aim of the article is to trace the integration of education, science and production through the development of regional cluster policy. At the present stage of development of postindustrial society in the global economy, the processes of globalization and specialization of national markets significantly increase competition between countries, between regions and between producers within the country. In these circumstances, the state authorities of the Russian Federation, while maintaining global leadership in the energy sector, define as long-term development goals of (...) the country, the creation of a competitive economy based on knowledge and high technologies. Following this doctrine in the Russian regions in the period from 2012 to 2014 cluster policy is implemented on the basis of Federal and regional innovation strategies. An important role in this process belongs to the activities of leading higher educational institutions - national research universities, which contribute to stimulate an active scientific activity of students and the introduction of scientific achievements into production. One of the pilot cluster projects, funded by the Federal budget became the territorial cluster ‘ Pharmaceutics, medical technology and information technology ‘ formed in the Tomsk region with a unique starting opportunities for the development of sustainable regional innovation system is a developed scientific and educational complex, small and medium-sized innovative enterprises with the latest technological developments in pharmaceuticals and medical technology. In two years, the activity cluster, implemented with the financial support of public authorities, has been successful. In the future, there are plans to expand the range of pharmaceutical products and the collaboration with other clusters Tomsk. Cluster policy contributes to the emergence of stakeholders and large economic enterprises, thus providing a close relationship between science, business and regional authorities. (shrink)

The early warning of financial risk is to identify and analyze existing financial risk factors, determine the possibility and severity of occurring risks, and provide scientific basis for risk prevention and management. The fragility of financial system and the destructiveness of financial crisis make it extremely important to build a good financial risk early-warning mechanism. The main idea of the K-means clustering algorithm is to gradually optimize clustering results and constantly redistribute target dataset to each clustering center (...) to obtain optimal solution; its biggest advantage lies in its simplicity, speed, and objectivity, being widely used in many research fields such as data processing, image recognition, market analysis, and risk evaluation. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of financial risk early-warning, elaborated the development background, current status and future challenges of the K-means clustering algorithm, introduced the related works of similarity measure and item clustering, proposed a financial risk indicator system based on the K-means clustering algorithm, performed indicator selection and data processing, constructed a financial risk early-warning model based on the K-means clustering algorithm, conducted the classification of financial risk types and optimization of financial risk control, and finally carried out an empirical experiments and its result analysis. The study results show that the K-means clustering method can effectively avoid the subjective negative impact caused by artificial division thresholds, continuously optimize the prediction process of financial risk and redistribute target dataset to each cluster center for obtaining optimized solution, so the algorithm can more accurately and objectively distinguish the state interval of different financial risks, determine risk occurrence possibility and its severity, and provide a scientific basis for risk prevention and management. The study results of this paper provide a reference for further researches on financial risk early-warning based on K-means clustering algorithm. (shrink)

This paper replies to objections from Thomas Adajian, Stephen Davies, and Robert Stecker to my claim, defended in ‘"Art" as a Cluster Concept’, that ‘art’ is a cluster concept and so cannot be defined. The paper also clarifies and extends the arguments of the earlier paper and locates its position in relation to the work of Morris Weitz.

This article starts with the environmental changes in human cognition, analyzes the virtual as the main feature of visual perception under digital technology, and explores the transition from passive to active human cognitive activities. With the diversified understanding of visual information, human contradiction of memory also began to become prominent. Aiming at the problem that the existing multimodal TV media recognition methods have low recognition rate of unknown application layer protocols, an adaptive clustering method for identifying unknown application layer (...) protocols is proposed. This method clusters application layer protocols based on similarity of the load characteristics of network stream application layer protocol data. The method divides the similarity calculation in the clustering algorithm to improve the clustering efficiency of the algorithm. Experimental results show that the proposed method can efficiently and accurately recognize unknown visual communication. This article proposes that, in the interactive multimodal visual information transmission, human visual perception experience has changed, the diversity of visual information content expression makes the aesthetic subject more personalized and stylized. (shrink)

Berys Gaut has recently defended a cluster account of art. He proposes it as superior to other anti-essentialist positions. I argue that his defence of this claim is unconvincing. Not only is the cluster theory consistent with the current crop of disjunctive definitions, it is at its most plausible when seen in such terms.

Photovoltaic power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder and Bayesian regularized NARX network for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance is applied to classify the dataset into multiple (...) clusters, including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE-BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R2 for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC-MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods. (shrink)