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Renewable energy production throughout low-voltage grids has gradually increased in electrical distribution systems, therefore introducing small energy producers - prosumers. This paradigm challenges the traditional unidirectional energy distribution flow to include disperse power production from renewables. To understand how energy usage can be optimized in the dynamic electrical grid, it is important to understand the behavior of prosumers and their impact on the grid's operational procedures. The main focus of this study is to investigate how grid operators can obtain an automatic data-driven system for the low-voltage electrical grid management, by analyzing the available grid topology and time-series consumption data from a real-life test area. The aim is to argue for how different consumer profiles, clustering and prediction methods contribute to the grid-related operations. Ultimately, this work is intended for future research directions that can contribute to improving the trade-off between systematic and scalable data models and software computational challenges. [ABSTRACT FROM AUTHOR]

Gene expression responses of paper birch (Betula papyrifera) leaves to elevated concentrations of CO2 and O3 were studied with microarray analyses from three time points during the summer of 2004 at Aspen FACE. Microarray data were analyzed with clustering techniques, self-organizing maps, K-means clustering and Sammon''s mappings, to detect similar gene expression patterns within sampling times and treatments. Most of the alterations in gene expression were caused by O3, alone or in combination with CO2. O3 induced defensive reactions to oxidative stress and earlier leaf senescence, seen as decreased expression of photosynthesis- and carbon fixation-related genes, and increased expression of senescence-associated genes. The effects of elevated CO2 reflected surplus of carbon that was directed to synthesis of secondary compounds. The combined CO2 +O3 treatment resulted in differential gene expression than with individual gas treatments or in changes similar to O3 treatment, indicating that CO2 cannot totally alleviate the harmful effects of O3. [Copyright &y& Elsevier]

This paper overviews an assortment of recent research work undertaken on recommender system models based on using multiple views of user and item-related data across the recommendation process. A summary of representative literature on multi-view recommender approaches is provided, describing their main characteristics, such as: their potential to overcome most common shortcomings in conventional recommender systems, as well as the use of data science, learning techniques and aggregation processes to combine information stemming from multiple views. A tabular summary is provided to facilitate the comparison of the similarities and differences among the surveyed works, along with commonly identified directions for future research in the topic. [ABSTRACT FROM AUTHOR]

This paper introduced automated detection and analysis of lunar craters are crucial for advancing lunar research and facilitating mission planning. This study introduces a comprehensive multi-scale approach for crater detection on the lunar surface using LROC (Lunar Reconnaissance Orbiter Camera) Dataset, enabling the identification of craters across varying scales and enhancing overall detection accuracy and efficiency. The process involves preprocessing lunar imagery to enhance features and delineate potential crater regions, utilizing edge detection algorithms to extract crater boundaries. Clustering techniques are then applied to group similar edge points and isolate potential crater candidates. A thresholding mechanism based on statistical edge intensity analysis refines the detection process. To address multiple detections of the same crater at slightly different diameters, radius criteria are implemented, yielding high diameter accuracy of 84% for mare Imbrium (test site 1) and 80% for mare Nubium (test site 2). Subsequently, a depth-diameter analysis validates crater-like characteristics by combining depth measurements with crater diameter estimation, showcasing the authenticity of potential craters. we compare our diameter and depth with different reference papers and we get high accuracy of R2 =0.99 then other methods. The results highlight the potential of this multi-scale approach for automated lunar crater detection, providing deeper insights into the Moon's surface morphology and history for future lunar exploration missions. [ABSTRACT FROM AUTHOR]

The collection of SuperDARN ionospheric echo data to make ionospheric convection maps is of great significance for Space Weather research. The ionospheric echoes for SuperDARN are generally mixed with scatter from the ground or sea surface, thus the clustering analysis of SuperDARN backscatter echoes is important. In this paper, the first implementation of deep clustering based on the graph automatic encoder network is efficiently realized for classifying the SuperDARN data. In addition, the model is compared with the traditional algorithm and machine learning clustering algorithms. Application of the model to sample data reveals that the deep clustering algorithm can capture the structural characteristics of the echoes and improve the accuracy of echo clustering and classifying. [ABSTRACT FROM AUTHOR]

The applications of clustering in biomedical is pervasive and ubiquitous. A typical example is gene expression data analysis, where clustering is emerging as a powerful solution for uncovering cancer-related insights. Neutrosophic c -means (NCM) clustering has advantages over other conventional clustering methods in characterizing the uncertainty and imprecision caused by cluster overlap and identifying outliers. Nonetheless, NCM and its derivatives equally treat the contribution of each attribute to the cluster. In biomedical applications, genes (i.e. attributes) should take different importance in identifying different clusters. In this paper, we first propose a self-adaptive attribute-weighted neutrosophic c -means (AWNCM) clustering method to overcome the above defects. Moreover, a new objective function is designed to obtain optimal neutrosophic partition, cluster centers and attribute weights. Since AWNCM tends to be more effective against spherical data, we further develop a kernelized version of AWNCM, called KAWNCM, in order to better satisfy the clustering of some complex data (i.e. non-spherical data). We employ the iterative optimization strategy to obtain the optimal solutions for AWNCM and KAWNCM. The advantage of AWNCM and KAWNCM is to improve performance by learning the importance of each attribute to the cluster while maintaining an efficient solution to cluster overlap and outliers. Extensive experimental results using synthetic data and gene expression data demonstrate the feasibility and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Intellectual analysis of small and middle-sized datasets through machine learning tools presents challenges in various application domains. Existing methods fail to provide sufficient accuracy, and their utilization is accompanied by a range of issues during data analysis. This paper proposes the improvement of the input doubling method for middle-sized data analysis. The existing method employs an augmentation procedure where the augmented data sample increases quadratically. This imposes several limitations on the method's usage for middle-sized data analysis. The authors propose enhancing this method by introducing an additional clustering procedure during data augmentation. The training algorithms and application methods are described, and a visualization of the main steps of its operation is provided. Modeling is performed on two medium-sized datasets. Optimal parameters for the improved method are selected, demonstrating its high efficiency. Specifically, significant reductions in the volumes of augmented datasets (8-9 times for both datasets respectively) are achieved, accompanied by substantial reductions in the training procedure duration of the method (more than 100 and 260 times for both datasets respectively), while maintaining high accuracy. [ABSTRACT FROM AUTHOR]

As the digital and physical worlds connect, machine-learning tools are increasingly being used by businesses and institutions, including archaeological research institutes and museums. The article presents research that, based on the evolution of human scripts, provides a tool for identifying script variants used in old manuscripts and stone inscriptions. This makes the work of paleography institutes and individual researchers more efficient. Using this research, it is possible to provide some information about millions of hitherto unidentified inscriptions using machine learning. The developed and tested method applies the biological phylogenetics tool to script evolution, an area that has yet to be investigated with machine learning tools. The final aim of the research is to use these tools to clarify the connections between many Arabic, Aramaic and Central Iranian script variants. The described procedure gives a new solution to one of the fundamental issues of phylogenetic analysis, feature selection, the effectiveness of which is verified with test runs. The paper presents a new feature selection method to identify the strength or effect of features before phylogenetic processing. This type of dimensionality reduction removes unnecessary features from the dataset for these analyses and interpretations. The method was applied to some versions of Arabic, Aramaic and Middle Iranian scripts, but the procedure is not script-specific. [ABSTRACT FROM AUTHOR]

Clustering and routing algorithms are critical to collecting and processing data sensed by nodes of wireless sensor networks deployed in various missions including environmental monitoring, tracking, and rescue operations. In this paper, a clustering algorithm is presented for wireless sensor networks with balanced cluster size resulting in energy efficiency. The algorithm is based on forming clusters using topology of the network and routing aggregated data from each cluster head to the base station. The cluster formation phase involves using tree-based traversals on the underlying sensor network graph. Using simulations, it is demonstrated that clusters of similar sizes are obtained for networks of varying sensor radii. It is demonstrated that the time to death of the first node of this clustering algorithm is superior to well-known clustering algorithm for random networks. [ABSTRACT FROM AUTHOR]

In the paper, we describe the procedure of determining reference spectra for medical diagnosis. In our research, we have used various clustering methods to extract spectra of a group of healthy patients participating in the determination of the final reference spectra. The results presented in the paper can be used in further research concerning the development of diagnostic algorithms based on comparing the spectra of the subjects, suspected by a specific disease unit, with developed reference spectra of healthy people. [ABSTRACT FROM AUTHOR]

Constrained machine learning (ML) models consists of incorporating a set of constraints into the ML model. The latter is generally used to incorporate domain knowledge, enhance performance, and define fair and robust ML models. Several papers reported that defining a full set of constraints is challenging. To address this issue, approaches for learning constraints were proposed. These approaches established general rules governing the data and dismissed any sample-related information. However, in user profiling tasks it is important to understand the position of each individual. In this paper, we define behavioral constraints as the set of numerical values that reflect the characteristics of each individual in the dataset. We propose an unsupervised approach for learning these behavioral constraints (ULBC). Applied to two datasets, the approach effectively provided behavioral constraints that unravel the similarities and dissimilarities between individuals. [ABSTRACT FROM AUTHOR]

This paper provides a detailed literature survey about main web service approaches targeting network traffic reduction. Technically, web service has suffered from network congestions and bottlenecks as a result of their high redundant textual structures. In fact, twenty seven highly reputed publications are discussed in this literature survey paper focusing on significant approaches and models that aim to reduce network traffic. Furthermore, publications are categorized into: web service clustering techniques for traffic reduction, web service clustering techniques for service discovery, aggregation approaches, and compression approaches. Evaluation analysis has shown a potential performance and efficiency for web service clustering techniques for traffic reduction in comparison with other models. Evidently, web service clustering models have achieved a tremendous compression ratio up to 21 with an average of 11.9. [ABSTRACT FROM AUTHOR]

• IGSC divides the document's structure that meets the user's grouping preferences. • IGSC learns the user's global intention by the deep metric learning network. • IGSC learns the intention-guided document feature representation. The intention expresses the user's preference for document structure division. Intention-guided document structure division is an important task in the field of text mining. To achieve this goal, deep semi-supervised document clustering provides a promising solution to personalized document clustering. However, traditional deep semi-supervised clustering models suffer from the problem of the limited number of constraints which is insufficient for intention-guided document clustering. Moreover, documents normally have various emphases on their representations to reflect different structural opinions. In this paper, we proposed an intention-guided deep semi-supervised document clustering model, namely IGSC, to divide document structure based on a small amount of user-provided supervised information. IGSC designs a deep metric learning network to solve the above problems. The deep metric learner explores the user's global intention and outputs an intention matrix. The intention is explored from the small amount user provided pairwise constraints and is used to guide the representation learning. Moreover, IGSC uses the intention matrix to guide the clustering process, to get the clustering results that best meet the user's intention. This paper compares IGSC with a number of document clustering models on four real-world text datasets, namely Reu-10k, BBC, ACM, and Abstract. The results show that IGSC evidently improves the clustering performance and outperforms the best result of benchmark models with 7% on average. The comparison with other models and the visualization results can demonstrate that IGSC is effective. [ABSTRACT FROM AUTHOR]

Clustering is a crucial technique used to identify the structure of data sets. It involves the task of splitting groups into different clusters based on suitable similarity measures. Fuzzy C-Means is a clustering technique that extends the fuzzy set theory. It is a widely used method among existing fuzzy clustering algorithms due to its easy implementation and straightforward approach. However, it still has some drawbacks, such as high sensitivity to outliers or noisy data, as well as to initialization conditions. Therefore, this paper proposes a modification to the Fuzzy C-Means algorithm using Fuzzy Chebyshev. The preprocessing of raw datasets includes the use of MinMax Scaler. Additionally, dimensional reduction techniques are applied to effectively address issues related to high-dimensional data. To determine the optimal number of clusters in the data set, the Elbow method is implemented. The final clustering is achieved by employing Fuzzy Chebyshev to replace the distance measurement component in the Fuzzy C-Means algorithm. A comparison between the proposed method and previous methods is performed. The effectiveness of the proposed method is illustrated through a numerical example involving genomic data of prostate cancer. Comparable results are presented to validate the feasibility of the proposed method. The results demonstrate that the proposed method aligns with those obtained from previous methods. [ABSTRACT FROM AUTHOR]

The work presented in this paper is part of a project that focuses on sorting simulation models used at Intel to calculate power consumption of electronic devices. Hundreds of thousands of analytical power models have already been accumulated in a directory of files and folders, for the simulation of the consumption of thousands of products. As a first step, this work focuses on comparing and grouping together models of smaller granularity - the formulas - which consist of power equations, the definition of the parameters involved in these equations, and a description. To manage this large data problem, we follow the principles of Model Management and Analytics, to decompose the problem into the analysis of constituent fields. We believe that the equations describing power consumption are the most highly information-rich field of our models. The aim of this work is to group together the formulas models, using Mathematical Language Processing (MLP), to take advantage of the business information contained in electrical equations. We calculate the embeddings of our formulas, to enable comparisons between them, to cluster together similar equations (and therefore models). [ABSTRACT FROM AUTHOR]

This paper provides a method for predicting the energy efficiency of buildings using artificial intelligence tools. The scopes is twofold: prediction of the levels of the heating load and cooling load of buildings. A feature of this research is the performance of intellectual analysis in conditions of a limited amount of data when solving the stated tasks. An improved method of augmentation and prediction (input-doubling method) is proposed by processing data within each cluster of the studied dataset. The selection of the latter occurs due to the use of the fast and easy-to-implement k-means method. Next, a prediction is made using the input-doubling method within each separate cluster. The simulation of the method was performed on a real-world dataset of 768 observations. The proposed approach was found to have a high prediction accuracy in the absence of overfitting and high generalization properties of the improved method. Comparison with existing methods showed an increase in accuracy by 40-46% (MSE) compared to SVR with rbf kernel, which is the basis for the improved method, and by 5-12% (MSE) compared to the closest existing hierarchical predictor. [ABSTRACT FROM AUTHOR]

Collaborator recommendation helps scholars find suitable collaborators, which is useful for the development of scientific research and the transformation of scientific and technological achievements. However, the gap between scholars' academic level seriously affects the establishment of cooperative relations. In this paper, a new model named Best Matching Collaborator Recommendation (BMCR) is proposed. BMCR model looks for the best matching collaborators for scholars from a new perspective of academic level. First, we define the academic level index of scholars and cluster scholars with this index. Next, the research topics of scholars are extracted from their published papers by using the topic extraction model. Then, for each cluster, a scholar-topic graph is built with the scholars and their topics. Finally, given the target scholar, the biased random walk with restart algorithm is carried out on the scholar-topic graph to find potential collaborators. Experiments on the MAG, DBLP, C-DBLP datasets show that our model improves the feasibility of cooperation between the target scholars and recommended scholars. Compared with other methods, our model has improved precision rate, recall rate and F1 score by 5.3%, 2.5%, and 4% respectively. The experimental results indicate that scholars' academic level is a key reference factor in the collaborator recommendation, and that scholars with similar academic level are more likely to achieve practical cooperation. The findings are not only of great significance in recommending collaborators to scholars, but also can be extended to other fields to solve the problem of collaborator recommendation. [ABSTRACT FROM AUTHOR]

[Display omitted] • Classification • Clustering • Energy efficiency • Group leader election • Intelligent System • Meta-heuristic • Wireless sensor networks Strategies to streamline energy resources in wireless sensor networks (WSN) are still a hot topic. Many researchers have proposed various models of the WSN protocol so that the base station (BS)/sink can receive data collected from each sensor node. The energy in the WSN, which has a long life span, will convey a lot of data to the sink until the sensor nodes die. This paper has proposed modeling the WSN protocol by utilizing the classification and clustering of sensor nodes using the chimpanzee leader election optimization (CLEO) method. The CLEO algorithm is a meta-heuristic algorithm inspired by a social model that applies the pattern of leader selection to the chimpanzee community. The optimization method has the advantage of getting a fast fitness value which is one of the critical factors in utilizing meta-heuristic models in the WSN field. The closest sensor nodes at a certain distance to the sink are marked as classification, while other sensor nodes are marked as clusters. The clustering of sensor nodes utilizes a cluster function using the CLEO algorithm. This paper also carried out the selection of group leaders to obtain an energy efficiency model from WSN-CLEO. The results of the proposed method, WSN-CLEO, have been compared to other protocol models. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to describe a methodology that was developed to support maintenance decision-making methods based on equipment condition. Condition-Based Maintenance allows to increase equipment availability and maximize investments. This is mainly due to the prevention of unexpected equipment downtime. By avoiding turning on/off industrial equipment, production flows are more efficient, allowing manufacturers to improve the quality of the end-product. The industry aims more and more to correspond satisfying customer expectations. We argue that the methodology developed in this paper adds value to the existing literature, namely because the fact that it is possible to anticipate the state of an equipment without a large amount of support data. In other words, although one could find information gaps regarding the occurrence of failures, it was possible to accurately assess the state of the equipment. This approach is robust, as it can be used in distinct equipment with different sensors, making this methodology generalizable for Condition-Based Maintenance. The paper presents the validation of the preceding through a case study on drying presses in the paper industry. To do so, three states were adopted, namely: "Proper function"; "Alert state"; and "Equipment failure". The methodology follows a series of steps, going through the collection of values from vibration sensors, imputation of values using Deep Artificial Neural Networks through on-line sensors, until reaching the last stage of classification carried out by the Hidden Markov Model. Through optimized observations from the previous steps, it was possible to define the hidden states through the Viterbi algorithm, which corresponds to the health states of the equipment. Additionally, it was possible to demonstrate that the proposed methodology can accurately characterize the condition states of the equipment based on the data obtained and can be generalized to other types of equipment. • Method classifies equipment status without prior info and predicts anomalies using ML. • Using DNN, time series data management was added to better diagnose equipment condition. • Generic method requires no technical knowledge, works online for real-time predictive maintenance. • The algorithm is generic and applicable to various equipment with different sensors. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach towards real-time earthquake forecasting in Chile by integrating intelligent technologies and machine learning methods. Earthquakes pose significant risks to communities and infrastructure in Chile, making accurate and timely forecasting crucial for disaster preparedness and mitigation. Traditional forecasting methods have limitations in providing real-time insights into seismic activity. In contrast, intelligent technologies such as artificial intelligence (AI) and machine learning offer promising avenues for enhancing prediction accuracy and speed. A new earthquake forecasting method using a modified clustering approach LMSCAN(Local Maxima-based Spatio-Cluster Analysis Network) and enhanced neural network time series analysis LSTM-IC (Long Short Term Memory Inverse Correlation) is presented in this paper. This neural network-based technology has been utilized to forecast earthquakes in the Chile region, one of the countries with the largest seismic activity. This study explores the integration of intelligent systems with machine learning algorithms to analyze seismic data and predict earthquake occurrences in Chile. By leveraging historical seismic data, sensor networks, and advanced predictive models, our approach aims to provide timely warnings and insights into seismic events, thereby improving disaster response and resilience. The proposed framework holds the potential to revolutionize earthquake forecasting by enabling real-time monitoring and proactive measures to safeguard communities and infrastructure in Chile and beyond. The remarkable 95 % accuracy achieved by this model is a testament to its exceptional learning process, which sets it apart from other models. Its ability to learn and adapt to new data is unparalleled, allowing it to forecast incredibly precisely and produce highly reliable results. [ABSTRACT FROM AUTHOR]

Word Sense Disambiguation (WSD) is significant for improving the accuracy of the interpretation of a Natural language text. Various supervised learning-based models and knowledge-based models have been developed in the literature for WSD of the language text. However, these models do not provide good results for low-resource languages, due to the lack of labelled and tagged data. Therefore, in this work, we have examined different word embedding techniques for word sense disambiguation of the Hindi language texts. Several studies in the literature show that these embeddings have been utilized for different foreign languages in the field of word sense disambiguation. However, to the best of our knowledge, no such work exists for the Hindi language. Therefore, in this paper, we utilize various existing word embeddings for WSD of Hindi text. Moreover, we have created Hindi word embeddings on articles taken from Wikipedia and test the quality of the created word embeddings using Pearson correlation. In this direction, we perform different experiments and observe that Word2Vec model gives best performance among all the considered embeddings on the used Hindi dataset. In our method, the proposed model directly takes input that is trained with word embedding methods and helps to develop a sense inventory using clustering that has been employed for performing disambiguation. Experimental observations indicate that the performance of the proposed approach is moderate and competent in terms of accuracy. The paper, thus, presents how WSD can leverage these representations to encode rich semantic information. [ABSTRACT FROM AUTHOR]

In real hyperspectral images, which have a complex background with multiple classes, separation of anomalous targets from background is a challenging problem. The Clustering based Background Learning (CBL) method is proposed for hyperspectral anomaly detection in this paper to deal with this difficulty. The proposed CBL method identifies the background classes, and so facilitates detection of the anomalies. CBL is built based on this reality that anomalies comprise a limited number of pixels occupying small areas. To achieve anomalies, the spectral and spatial clustering maps are fused through a new method called Multiplicative Fusion (MF). This fusion method leverages both spectral and spatial information and makes the clustering results to support each other for anomalies detection. The synergy done in the clustering phase followed by applying an adaptive support vector machine leads to identification of various background classes. One important advantage of the CBL method is its generalization ability for anomaly detection in various sub regions of a large scene where the trained model for a sub region can be used for anomaly detection of other sub regions of the same scene. Additionally, the pre-processing and the model training steps can be performed on a common hardware without any requirement to expensive hardware for high processing power. The experimental results demonstrate that the CBL method outperforms the competing methods on various types of hyperspectral data with different spectral and spatial resolutions where a stable behaviour can be seen in detection performance of CBL on different images. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) includes more low-cost and less-power sensor nodes. All the sensor nodes are positioned in a particular area and form a wireless network by way of self-organizing. They has the ability to work normally at any of the special or wicked environ that people cannot close. However, the data transmission among nodes in an effective way is almost not possible due to various complex factors. Clustering is a renowned technique to make the transmission of data more effective. The clustering model divides the sensor nodes into various clusters. Every cluster in network has unique cluster head node, which send the information to other sensor nodes in cluster. In such circumstances, it is the key role of any clustering algorithm to choose the optimal cluster head under various constraints like less energy consumption, delay and so on. This paper develops a new cluster head selection model to maximize the lifetime of network as well as energy efficiency. Further, this paper proposes a new Fitness based Glowworm swarm with Fruitfly Algorithm (FGF), which is the hybridization of Glowworm Swarm Optimization (GSO) and Fruitfly Optimization algorithm (FFOA) to choose the best CH in WSN. The performance of developed FGF is compared to other existing methods like Particle swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), GSO, Ant Lion Optimization (ALO) and Cuckoo Search (CS), Group Search Ant Lion with Levy Flight (GAL-LF), Fruitfly Optimization algorithm (FFOA) and grasshopper Optimization algorithm (GOA) in terms of alive node analysis, energy analysis and cost function and the betterments of proposed work is also proven. [ABSTRACT FROM AUTHOR]

The rapid advance of technology has led to exponential growth in the generation of data in various fields, revolutionising how information is captured, processed and analysed. The continuous flow of data has proven to be a valuable resource for agile decision-making. The complexity of being able to make decisions from different sources has led to the emergence of new techniques that aim to obtain as much information as possible to relate data efficiently, taking into account multiple perspectives. A new approach to clustering streaming data from different perspectives is introduced in this paper. It is based on evolutionary algorithms (CHC), fuzzy rules and consensus function. The proposed approach is called FuzzyMultiCHCClust-DS. This approach addresses the clustering of streaming data by optimising the clustering process taking into account traditional clustering perspectives such as k-means, Clara, Pam, Fanny and MiniBatchKMeans. It has been tested using different sets of linguistic labels, achieving a high degree of compactness and separation of the cl ustered data. [ABSTRACT FROM AUTHOR]

As an important part of machine learning, clustering methods have been continuously paid attention to. Current clustering methods divide data objects usually based on Euclidean metric, which is a basic and effective metric method. However, with the high dimensionality of data and the diversification of data representation, the complexity of the spatial structure of real-world data continues to rise. Classical clustering methods face many challenges such as insufficient clustering effectiveness, the sensitivity of clustering method parameters, and lack of stability of clustering results. Aiming at the above problems, this paper designs a non-Euclidean metric and constructs a multi-granularity staged clustering method based on the metric. First of all, this paper uses the sequential relationship of each feature of the data to construct a similarity measure between objects from the perspective of positive and negative granularity to improve the clustering algorithm's understanding of complex spatial structure data. Secondly, this paper designs the attenuation-diffusion pattern divides and conquers according to the distribution characteristics of data objects in different patterns, and uses the heuristic idea to effectively cluster the data in stages from local to global. Again, based on the above, this paper proposes a clustering method based on multi-positive-negative granularity and attenuation-diffusion pattern, which can effectively deal with the challenges brought by complex spatial structure data to clustering methods. Finally, the effectiveness and robustness of the proposed method and advanced clustering methods are compared and analyzed on UCI real data sets. Experimental results show that the method proposed in this paper has obvious advantages in clustering results on complex spatial structure data. In addition, in the two directions of non-Euclidean metrics and multi-granularity clustering, the method proposed in this paper provides a new perspective for effectively dealing with the design of clustering methods on complex spatial structure data. • We design a non-Euclidean metric. • We construct a multi-granularity staged clustering method. • We provide a clustering methods on complex spatial structure data. [ABSTRACT FROM AUTHOR]

Uncertainty and imprecision accompany the world we live in and occur in almost every event. How to better interpret and manage uncertainty and imprecision play a vital role in machine learning (ML). As an effective tool for modeling imperfection, the theory of belief functions (TBF) has attracted substantial attention by providing a flexible discernment of framework for effectively representing uncertainty and imprecision. To date, many TBF-based methods have been proposed in ML, but they have not yet been comprehensively summarized. This paper surveys TBF-based methods for representing uncertainty and imprecision in ML, focusing on clustering, classification and information fusion. First, we provide a formal definition of uncertainty and imprecision reasoning. On this basis, we survey the existing TBF-based methods in detail and explain how to characterize uncertainty and imprecision in the results. What is more, we discuss the current challenges in TBF-based ML and offer insightful perspectives for future research regarding clustering, classification and information fusion. This survey not only fills a critical gap in the existing literature but also serves as a guiding beacon for future explorations, emphasizing the transformative role of TBF in advancing ML methodologies. • The paper provides a survey of representing uncertainty and imprecision in machine learning with belief functions. • The paper proposes the formal definition of uncertainty and imprecision reasoning. • The paper covers popular and efficient methods in clustering, classification and information fusion with belief functions. • The paper summarizes the promising research directions in the future. [ABSTRACT FROM AUTHOR]

The availability of a variety of Internet-based services for tourists has drastically changed the way travel plans are made. This has indicated that instead of travel planning from traditional travel information (articles and guidebooks), it is starting from emotional information linked to photographs and other imagery information. In this study, in order to provide an "EmoTABI experience," a travel to feel EMOI, we have developed to analyze image of interest, extract a color emotion derived from the color information of image and object emotion related to objects in the image, respectively, and search for optimal sightseeing spots match the mental value of before travel, TABIMAE. Using this developed the TABIMAE mind search system, recommendations for tourist destinations were provided for Tokyo, Osaka, and Nasu Town, Tochigi Prefecture. Furthermore, we carried out a questionnaire survey to confirm the validity of the system. As a result, we confirmed that the results obtained from this system were valid. In this paper, the TABIMAE mind search system is overall and reported to discuss the obtained results through the prototype system. [ABSTRACT FROM AUTHOR]

Concept hierarchies, as part of knowledge representation methods, play an important role in supporting the exchange and sharing of information. We developed and automated a concept hierarchies construction process which includes several artificial intelligence techniques. When automating their construction from an existing, more or less structured, body of knowledge, the evaluation of the resulting concept hierarchy is an important step. We propose an approach for concept hierarchy construction (CHC) from short sentences, that makes use of methods like Sentence Embedding, Clustering, and Automatic Labeling to create a hierarchical representation consisting of three layers. Our major focus in this paper is not on the algorithms used but on their evaluation using manual clustering by experts and fuzzy sets. [ABSTRACT FROM AUTHOR]

The GakuNin LMS operated by the National Institute of Informatics (NII) provides an information security teaching course called "Princess Rin-Rin's security column". Gunma University extensively encourages students, faculty members, and staff to take this course. In this paper, we have classified the participants who took this course at Gunma University in the first semester of 2022 according to their course status and the results of the final test. Hierarchical clustering and K-means clustering, which are unsupervised learning methods, were used for the classification. The course status was classified into eight clusters, and the result of the final test was into four clusters. We have identified the most excellent cluster of the final test by investigating the clusters where the participants complete the final test with fewer attempts and in a shorter period of time. Furthermore, we have identified clusters of course status that include relatively good participants compared to the overall average using the relation between the clusters of course status and the clusters of final tests. [ABSTRACT FROM AUTHOR]

In this paper, we examine the efficacy of unsupervised learning approaches, particularly clustering and dimensionality reduction techniques, in practical applications such as image compression and geospatial data visualization. Initially, we scrutinize the applicability of the elbow rule in determining the optimal number of clusters within synthetic datasets of varying structures using the k-means algorithm. Subsequently, we evaluate the potency of density-based (DBSCAN) and hierarchical clustering algorithms in unearthing intrinsic patterns within these datasets. Shifting our focus towards practical applications, we leverage the k-means clustering for image compression, demonstrating a notable reduction in storage requirements without a significant compromise in visual quality. Our findings articulate that a strategic selection of cluster numbers can yield substantial compression rates. In the final segment of our investigation, we delve into the utility of dimensionality reduction techniques, specifically t-SNE and UMAP, in the realm of geospatial data visualization. Utilizing a dataset comprising distances between Spanish provinces, we gauge the proficiency of these techniques in preserving relative distances upon projection onto a two-dimensional plane. Our observations denote that both t-SNE and UMAP can generate precise visual representations of the original geographic layouţ with UMAP exhibiting exceptional performance in comparison with alternative methodologies. On a broader scale, our study underscores the versatility and practical utility of unsupervised learning techniques across an array of applications, ranging from image compression to geospatial data visualization. It emphasizes the significance of comprehending their foundational mechanisms and fine-tuning their hyperparameters to achieve optimal performance. [ABSTRACT FROM AUTHOR]

In a world of connectivity empowered by the advancement of the Internet of Things (IoT), an infinite number of data streams have emerged. Thus, data stream clustering is crucial for extracting hidden knowledge and data mining. Various data stream clustering methods have lately been introduced. Yet, the majority of such algorithms are affected by the curse of high dimensionality. Lately, a fully online buffer-based clustering algorithm for handling evolving data streams (BOCEDS) was developed. Similarly to other existing density-based clustering methods, BOCEDS is not capable of handling high-dimensional data and has high computational power and high memory utilization. This paper introduces an Entropy Window Reduction (EWR) algorithm, which is an improved version of the BOCEDS technique. EWR is a fully online clustering technique for handling high-dimensional data streams using feature ranking and sorting. This process is accomplished by calculating the entropy of specific features with respect to the time window. The findings of the experiments are compared to the outcomes of BOCEDS, CEDAS, and MuDi-Stream algorithms. The outcomes indicate that the EWR algorithm outperformed the baseline clustering algorithms. The results are demonstrated using the KDDCup'99 dataset in terms of quality and complexity evaluation on the average of F-Measures, Jaccard Index, Fowlkes–Mallows index, Purity, and Rand Index as well as the memory usage and computational power with 88%, 66%, 81%, 100%, and 66%, respectively. The results also show low memory usage and computing power in comparison with the baseline algorithms. [ABSTRACT FROM AUTHOR]

Machine learning (ML) models are becoming increasingly complex. In fact, a sophisticated model (XGBoost boosting or deep learning) generally leads to more accurate predictions than a simple model (linear regression or decision tree). There is therefore a trade-off between the performance of a model and its interpretability: what a model gains in performance, it loses in interpretability (and vice versa), where interpretability is the ability for a human to understand the reasons for a model's decision. However, explaining the predictions made by machine learning models aims at computing and interpreting the importance of features. To achieve this, game theory has recently gained attention for better understanding the similarity between group members. In this paper, we use SHAP (SHapley Additive exPlanations), which is a method based on cooperative game theory, to analyze and evaluate the properties of each group. More importantly, we rely k-means PCA and Light gbm classifier to improve the data preparation before grouping the features into multiple clusters. The simulation results prove the importance of shapley value in creating an accurate and meaningful representation of each cluster. [ABSTRACT FROM AUTHOR]

Energy minimization is one of the significant tasks in the WSN which can be achieved through clustering process. Clustering in the WSN is an efficient method to increase the lifespan of the network. In the proposed work the main concentration is given to the network lifetime which is very necessary for the less node failures and high packet deliveries. In this paper the hierarchical structuring is done using clustering approach based on efficient data aggregation process. The aggregation process is defined into two phases. The first phase defines the arrangement of the nodes in the cluster using uniform distribution and second phase defines the selection of the cluster head and aggregation of the data using linear programming. The main objective is to minimize the energy consumption and increase of the robustness of the sensor network. Eventually the performance of the network will be evaluated in terms of low energy consumption, high packet deliveries and low congestions in the WSN. [ABSTRACT FROM AUTHOR]

Coronary heart disease patients must reach a hospital within 90 minutes from the start of myocardial infarction to avoid the risk of death. Current hospital locations are optimized for the overall health care system and may not be the best possible for these patients. For this reason, we have constructed a web tool for optimizing the facility locations to study how well they serve for the coronary heart disease patients. In this paper, we explain how the system has been constructed, how it can be used, and show the results using patient data in Finland. Results show that the number of patients at risk could theoretically be reduced to 1% with optimized locations but at the cost of increasing the average travel time. [ABSTRACT FROM AUTHOR]

Recently, autonomous systems have received considerable attention amongst research communities and academicians. Unmanned aerial vehicles (UAVs) find useful in several applications like transportation, surveillance, disaster management, and wildlife monitoring. One of the important issues in the UAV system is energy efficiency, which can be resolved by the use of clustering approaches. In addition, high resolution remote sensing images need to be classified for effective decision making using deep learning (DL) models. Though several models are available in the literature, only few approaches have focused on the clustering and classification processes in UAV networks. In this aspect, this paper designs a novel metaheuristic with an adaptive neuro-fuzzy inference system for decision making named MANFIS-DM technique on autonomous UAV systems. The proposed MANFIS-DM technique intends to effectively organize the UAV networks into clusters and then classify the images into appropriate class labels. The proposed MANFIS-DM technique encompasses two major stages namely quantum different evolution based clustering (QDE-C) technique and ANFIS based classification technique. Primarily, the QDE-C technique involves the design of a fitness function involving three parameters namely average distance, distance to UAVs, and UAV degree. Besides, the image classification model involves a set of subprocesses namely DenseNet based feature extraction, Adadelta based hyperparameter optimization, and ANFIS based classification. The design of QDE-C algorithm with classification model for autonomous UAV systems show the novelty of the work. The experimental result analysis of the MANFIS-DM method is carried out against benchmark dataset and the results ensured the enhanced performance of the MANFIS-DM technique over the other methods with the maximum a c c u y of 99.13%. [ABSTRACT FROM AUTHOR]

• In this paper, an improved clustering method based on biological principles is presented. • The approach incorporates two new mechanism for improving the performance of the original Cellular Clustering Algorithm. • The first mechanism is a pre-processing approach for reducing the dataset dimensionality. • The second process includes an optimization method to automatically find the best data resolution. A clustering methodology based on biological visual models that imitates how humans visually cluster data by spatially associating patterns has been recently proposed. The method is based on Cellular Neural Networks and some resolution adjustments. The Cellular Neural Network rebuilds low-density areas while different resolutions find the best clustering option. The algorithm has demonstrated good performance compared to other clustering techniques. However, its main drawbacks correspond to its inability to operate with more than two-dimensional data sets and the computational time required for the resolution adjustment mechanism. This paper proposes a new version of this clustering methodology to solve such flaws. In the new approach, a pre-processing stage is incorporated featuring a Self-Organization Map that maps complex high-dimensional relations into a reduced lattice yet preserving the topological organization of the initial data set. This reduced representation is employed as the two-dimensional data set for further processing. In the new version, the resolution adjustment process is also accelerated through the use of an optimization method that combines the Hill-Climbing and the Random Search techniques. By incorporating such mechanisms rather than evaluating all possible resolutions, the optimization strategy finds the best resolution for a clustering problem by using a limited number of iterations. The proposed approach has been evaluated, considering several two-dimensional and high-dimensional datasets. Experimental evidence exhibits that the proposed algorithm performs the clustering task over complex problems delivering a 46% faster on average than the original method. The approach is also compared to other popular clustering techniques reported in the literature. Computational experiments demonstrate competitive results in comparison to other algorithms in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

Analyzing sensor data from a production environment is quite challenging because of the high-dimensional nature of the data. In addition, the generated data is in the form of time-series, where the sequence of registrations may be of utmost significance. One of the main goals of the paper is to determine if the given time-series of feature combinations is normal or rare. This goal could successfully be achieved by combining multiple machine learning models. In this paper, a sliding window based ensemble method is proposed to detect outliers in a streaming fashion. The proposed method uses a combination of clustering algorithms to construct subgroups (clusters) representing different data structures. These structures are later used in a one-class classification algorithm to identfy the outliers. Thus, if a pattern does not belong to any of the common structures or clusters, it is an outlier. Further, based on the rare pattern classification, machine failures could be predicted in advance. [ABSTRACT FROM AUTHOR]

Traditional clustering algorithms like K-medoids and DBSCAN take distances between objects as input and find clusters of objects. Distance functions need to satisfy the triangle inequality (TI) property, but sometimes TI is violated and, thus, may compromise the quality of resulting clusters. However, there are scenarios, for example in the context of social networks, where TI does not hold but a meaningful clustering is still possible. This paper investigates the consequences of TI violation with respect to different traditional clustering techniques and presents instead a clique guided approach to find meaningful clusters. In this paper, we use the quantum logic-based query language (CQQL) to measure the similarity value between objects instead of a distance function. The contribution of this paper is to propose an approach of non-TI clustering in the context of social network scenario. [ABSTRACT FROM AUTHOR]

Automatic grading is not a new approach but the need to adapt the latest technology to automatic grading has become very important. As the technology has rapidly became more powerful on scoring exams and essays, especially from the 1990s onwards, partially or wholly automated grading systems using computational methods have evolved and have become a major area of research. In particular, the demand of scoring of natural language responses has created a need for tools that can be applied to automatically grade these responses. In this paper, we focus on the concept of automatic grading of short answer questions such as are typical in the UK GCSE system, and providing useful feedback on their answers to students. We present experimental results on a dataset provided from the introductory computer science class in the University of North Texas. We first apply standard data mining techniques to the corpus of student answers for the purpose of measuring similarity between the student answers and the model answer. This is based on the number of common words. We then evaluate the relation between these similarities and marks awarded by scorers. We consider an approach that groups student answers into clusters. Each cluster would be awarded the same mark, and the same feedback given to each answer in a cluster. In this manner, we demonstrate that clusters indicate the groups of students who are awarded the same or the similar scores. Words in each cluster are compared to show that clusters are constructed based on how many and which words of the model answer have been used. The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer. We argue that computational methods be used to enhance the reliability of human scoring, and not replace it. Humans are required to calibrate the system, and to deal with situations that are challenging. Computational methods can provide insight into which student answers will be found challenging and thus be a place human judgement is required. [ABSTRACT FROM AUTHOR]

The Slope One algorithm in the recommendation system has the characteristics of real time efficiency, convenient operation, but at the same time there are not considering between project and the similarity between user's question. In order to improve such problems and improve the accuracy of the algorithm, this paper proposes a Slope One recommendation algorithm that integrates user clustering and scoring preference. First, the similarity among users is measured, and the improved K-means++ algorithm is used to divide users into several categories according to the similarity degree of project preferences. Then, in the category of target users, the Slope One algorithm that integrates users' rating preferences is used to predict the score of projects. And finally top-n recommendation is made according to the predicted score. In this paper, using the Movielens dataset to experiment, the results show that the proposed algorithm can effectively reduce the mean absolute error and root mean square error of traditional algorithm, recommend have higher accuracy and better recommendation quality. [ABSTRACT FROM AUTHOR]

• A new biomedical text summarization based on clustering and frequent itemsets mining. • Biomedical texts are represented using concepts instead of terms. • This combination enhances the quality of the generated summaries. • The clustering has a crucial impact on the discovered frequent itemsets. • Contribute all clusters in the sentence selection step yields to better performances. Text summarization has become an important research area, especially in the biomedical domain, where information overload is a major problem. In this paper, we propose a novel biomedical text summarization system that combines two popular data mining techniques: clustering and frequent itemset mining. Biomedical paper is expressed as a set of biomedical concepts using the UMLS metathesaurus. The K-means algorithm is used to cluster similar sentences. Then, the Apriori algorithm is applied to discover the frequent itemsets among the clustered sentences. Finally, the salient sentences from each cluster are selected to build the summary using the discovered frequent itemsets. For the evaluation step, we selected randomly 100 biomedical papers from the BioMed Central database full-text , and we evaluated the performances of our system by comparing the resulting summaries with the abstracts of these papers using the ROUGE metrics in term of recall, precision, and F-measure. We also compared the obtained summaries with those achieved by five well-known summarizers: TextRank, TextTeaser, SweSum, ItemSet Based Summarizer, Microsoft AutoSummarize , and two baselines: summarization using only the frequent itemsets mining (FRQ-CL), and summarization using only the clustering (CL-FRQ). The results demonstrate that this combination can successfully enhance the summarization performances, and the proposed system outperforms other tested summarizers. [ABSTRACT FROM AUTHOR]

Rescue station setup and rescue path planning are two important tasks in urban emergency rescue. The former task ensures rescue response capability and the latter task provides effective rescue solutions. When emergencies occur in cities, evacuees are distributed along the urban road network. Rescue resources refer to rescue vehicles whose available number and capacity are both limited. With the constraints of rescue resources and the number of rescues, this paper aims to simultaneously optimize the tasks of rescue station setup and rescue path planning. In the addressed scenario, the priority of each evacuee is quantified as a weight value that is used as the main optimization objective. To solve the problem, a comprehensive urban emergency rescue planning approach is proposed. The proposed approach consists of components of road network processing, road network weight calculation, rescue station setup and rescue path planning. For the setup of rescue stations, this paper employs a clustering method to provide a set of high-quality candidate rescue stations for subsequent path planning based on the locations of evacuees and the road network structure. For rescue path planning, an improved ant colony optimization algorithm is developed. The proposed method is called the planning algorithm with clustering and improved ant colony optimization (PA-C-IACO). The proposed PA-C-IACO redefines the degree of heuristic and pheromone concentration increments for transfer between intersections in the ant colony algorithm and incorporates a reward mechanism during the pheromone update process. Experimental results on six different size datasets show that PA-C-IACO outperforms state-of-the-art algorithms and shows good robustness and feasibility. • For urban emergency rescue problems, rescue response capability and the effectiveness of rescue solutions are considered simultaneously. • A hybrid clustering and improved ant colony optimization algorithm approach is proposed. • The emergency rescue problem is subdivided into multiple small problems and solved collaboratively. • A reward mechanism is devised to prevent falling into a local optimum. • An extensive experimental evaluation and comparison on a real dataset is provided. [ABSTRACT FROM AUTHOR]

Recently, low power wireless sensor networks (LPWSNs) have been widely used in the military and education. However, the lifetime of LPWSN is affected by the energy of the sensor, so the clustering design has received a lot of attention. In this study, the problem of designing optimal clustering for LPWSN is formulated as a cluster head selection problem, considering energy, which is an NP-hard problem. Therefore, a new structural model of the clustering design problem is constructed. This model can represent the process of sensor node clustering and information transfer. In this paper, a new immune chaotic adaptive evolutionary algorithm (ICAEA) is proposed and used in the clustering design of LPWSNs to obtain a better cluster head selection scheme. Then we design new advanced operators, such as immune operator and chaotic operator, which improve the convergence speed of the algorithm on the basis of evolutionary algorithm (EA). Moreover, ICAEA avoids local optima by introducing an adaptive operator, improves the convergence accuracy of the algorithm, and improves the performance of the optimization. Simulations are conducted to determine the performance improvements of ICAEA in terms of network lifetime and energy consumption compared to the latest clustering methods of R-LEACH, Q-LEACH, and ICCHR. The experimental results show that the proposed ICAEA algorithm outperforms RLEACH, Q-LEACH and ICCHR in terms of network lifetime under multiple identical experimental conditions. Moreover, ICAEA consumes less energy than R-LEACH, Q-LEACH and ICCHR in terms of system energy consumption. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks include a substantial number of nodes with limited battery power that is used for gathering and sending the data to the Base station. Here most energy is consumed in data transfer. Hence a foremost problem is the maximization of network lifetime through minimization of energy consumption in the nodes. To resolve this issue, the clustering method is involved to achieve energy-efficient data transmission. A cluster head is elected which uses an energy distribution mechanism to conserve the remaining power, thereby extending network lifetime. Various present methodologies are employed but every algorithm had notable constraints individually. In this paper, a hybrid Sparrow Search Algorithm with Differential Evolution algorithm is intended to solve the energy efficiency issue by cluster head selection in Wireless Sensor Networks. The proposed algorithm uses the high-level search efficiency of the Sparrow Search Algorithm and the lively potential of Differential Evolution that enhances the lifetime of nodes. The performance of this hybrid model seems to be exploiting the count of alive nodes and dead nodes, throughput, and residual energy. The proposed Improved Sparrow search algorithm using the Differential evolution model for choosing the best possible cluster head shows a development in residual power and throughput than other compared algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, group decision-making is an everyday event, since we usually live in a community. Nevertheless, when the number of participants is large, it starts to generate a lot of information that is complex to manage, and unlike group decision-making, where the participants are usually experts, when the number of participants increases, not all of them are, which generates a conflict of interest because the value of the vote of an expert and a (non-expert) user is worth the same. Moreover, it may happen that not all participants who start a process will finish it and not all of them will be able to make all the assessments, since they do not know all the options. Therefore, to solve all the drawbacks, this paper presents a large-scale group decision-making system in a modifiable scenario environment that groups participants according to their preferred alternative and also performs knowledge differentiation, clustering them into experts and users. This allows for optimising the consensus process, as the consensus is made by comparing the clusters and the participants with each other. [ABSTRACT FROM AUTHOR]

The standard NMF algorithm is not suitable for sampling data from low-dimensional manifolds embedded in high-dimensional environmental spaces, as the geometric information hidden in feature manifolds and sample manifolds is rarely learned. In order to obtain better clustering performance based on NMF, manifold and orthogonal constraint, a new type of model named Orthogonal Graph regularized Non-negative Matrix Factorization model under Sparse Constraints (OGNMFSC) is proposed. Firstly, this type of model constructs a nearest neighbor graph to encode the geometric information of the data space, in order to obtain more discriminative ability by preserving the structure of the graph. Secondly, this type of model adds orthogonal constraints to achieve better local representation and significantly reduce the inconsistency between the original matrix and the basis vectors. Thirdly, by adding sparse constraints to obtain a sparser representation matrix, the clustering performance of the model can be improved. The main conclusion of this paper is that two effective algorithms have been generated to solve the model, which not only provides theoretical convergence proof for these two algorithms, but also demonstrates significant clustering performance in experiments compared to classical models such as K-means, PCA, NMF, Semi-NMF, NMFSC, ONMF, GNMF, NeNMF. • Effectively combining graph regularization, orthogonality and sparsity. • The convergence proof of the new model's algorithm has been obtained. • Compared with NMF, ONMF and etc, this model has shown good results in experiments. • Explain the effective combination of orthogonality, graph, sparsity via experiments. [ABSTRACT FROM AUTHOR]

Dataflow Models of Computation (MoCs) significantly enhance parallel computing by efficiently expressing application parallelism on multicore architectures, unlocking greater performance and throughput. However, the complexity of graphs within dataflow-based systems can result in a time-consuming resource allocation process. To address this issue, a solution is to cluster computations to ease heuristic solving. The information encompassing the context of computations and the constraints of the architecture plays a crucial role in determining application performance. This paper presents an automated approach that leverages this information to control graph complexity prior to the resource allocation process. Experiments demonstrate that the proposed method, driven by clustering, not only yields improved throughput but also provides better mapping decisions and data transfer efficiency, achieving a throughput up to 1.8 times higher than state-of-the-art techniques. [ABSTRACT FROM AUTHOR]