Everyday, a large number of complex scientific and industrial problems involve finding an optimal solution in a large solution space. A challenging task for several optimizations is not only the combinatorial operation but also the constraints of ava...
The rapid growth in the industrial sector has required the development of more productive and reliable machinery, and therefore, leads to complex systems. In this regard, the automatic detection of unknown events in machinery represents a greater cha...
Aiming at improving the well-known fuzzy compactness and separation algorithm (FCS), this paper proposes a new clustering algorithm based on feature weighting fuzzy compactness and separation (WFCS). In view of the contribution of features to cluster...
The belief rule-based classification system (BRBCS) is a promising technique for addressing different types of uncertainty in complex classification problems, by introducing the belief function theory into the classical fuzzy rule-based classificatio...
A cognitive radio sensor network (CRSN) is a wireless sensor network whose sensor nodes are equipped with cognitive radio capability. Clustering is one of the most challenging issues in CRSNs, as all sensor nodes, including the cluster head, have to...
The generation and analysis of vast amounts of data have become increasingly prevalent in diverse applications. In this study, we propose a novel approach to address the challenge of rule explosion in association rule mining by utilizing the coverage...
Soils are a finite resource that is under threat, mainly due to human pressure. Therefore, there is an urgent need to produce maps of soil properties, functions and behaviors that can support land management and various stakeholders’ decisions....
This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on the concept size of given objects and/or clusters described using a fixed numbe...
Soil compaction is one of the most important and readily mitigated threats to soil health. Digital Soil Mapping (DSM) has emerged as an efficient method to provide broad-scale maps by combining soil information with environmental covariates. Until no...
In viticulture, the main target is to achieve high yield and good fruit quality without compromising vine growth. Methods to achieve this balance will vary with regard to climate and cultivar. A two-year study was conducted on five-year-old ‘Pr...
Color texture classification aims to recognize patterns by the analysis of their colors and their textures. This process requires using descriptors to represent and discriminate the different texture classes. In most traditional approaches, these des...
Multiple cluster validity indices (CVIs) have been introduced for diverse applications. In practice, clusters exhibit varying shapes, sizes, densities, and closely spaced centers, which are typically unknown beforehand. It is desirable to develop a v...
Cluster compactness is a trait with high agronomic relevance, affecting crop yield and grape composition. Rachis architecture is a major component of cluster compactness determinism, and is a target trait toward the breeding of grapevine varieties le...
Compactness and separability of data points are two important properties that contribute to the accuracy of machine learning tasks such as classification and clustering. We propose a framework that enhances the goodness criteria of the two properties...
Intrinsically disordered proteins (IDPs) are ensembles of interconverting conformers whose conformational properties are governed by several physico-chemical factors, including their amino acid composition and the arrangement of oppositely charged re...
In the present scenario, weight diminution and strength enrichment are the main requirements for escalating the application of a nano composite material in different sectors. Several industrial sectors, such as automobile, defense and aerospace, are...
The accelerating progress of the Internet of Vehicles (IoV) has put forward a higher demand for distributed model training and data sharing in vehicular networks. Traditional centralized approaches are no longer applicable in the face of drivers&rsqu...
Grapevine cluster compactness affects grape composition, fungal disease incidence, and wine quality. Thus far, cluster compactness assessment has been based on visual inspection performed by trained evaluators with very scarce application in the wine...
Dwarf galaxies are by far the most numerous galaxies in the Universe, showing properties that are quite different from those of their larger and more luminous cousins. This review focuses on the physical and chemical properties of the interstellar me...
Background: The Clustered Orienteering Problem is an optimization problem faced in last-mile logistics. The aim is, given an available time window, to visit vertices and to collect as much profit as possible in the given time. The vertices to visit h...
Due to the cluster reducibility of multiquark operators, a strong interplay exists in tetraquarks between the compact structures, resulting from the direct confining forces acting on quarks and gluons, and the molecular structure, dominated by the me...
Oversampling is common and effective in resolving the classification problem of imbalanced data. Traditional oversampling methods are prone to generating overlapping or noisy samples. Clustering can effectively alleviate the above problems to a certa...
Clustering is one of the main tasks of machine learning. Internal clustering validation indexes (CVIs) are used to measure the quality of several clustered partitions to determine the local optimal clustering results in an unsupervised manner, and ca...
Three optimization problems based on node-colored undirected graphs are the subject of the present study. These problems model real-world applications in several domains, such as cybersecurity, bioinformatics, and social networks, although they have...
Machine learning techniques are ever prevalent as datasets continue to grow daily. Associative classification (AC), which combines classification and association rule mining algorithms, plays an important role in understanding big datasets that gener...
Research on carbon emission is an important basis for solving global climate problems, and it is also one of the ways to quantitatively assess the impact of human activities. Cities are one of the main bearing spaces of human activities, and reasonab...
30 October 2019
The Yangtze River Delta (YRD) region is one of the most densely populated and economically developed areas in China, which provides an ideal environment with which to study the various strategies, such as compact and polycentric development advocated...
Chronic lymphocytic leukemia (CLL) is uncommon in Asia, and longitudinal genomic data from Asian cohorts are limited. We conducted serial whole-exome sequencing (WES) in a multicenter Korean cohort of newly diagnosed, elderly CLL treated with chloram...
We propose a novel method for adaptive K-means clustering. The proposed method overcomes the problems of the traditional K-means algorithm. Specifically, the proposed method does not require prior knowledge of the number of clusters. Additionally, th...
This paper introduces a new multiclass classifier called the evolving Fuzzy Classifier (eFC). Starting its knowledge base from scratch, the eFC structure evolves based on a clustering algorithm that can add, merge, delete, or update clusters (= rules...
The impact of hot-melt extrusion (HME) on the solid-state properties of four methacrylic (Eudragit® L100-55, Eudragit® EPO, Eudragit® RSPO, Eudragit® RLPO) and four polyvinyl (Kollidon® VA64, Kollicoat® IR, Kollidon® SR, a...
Fault concealment in complex software programs and the difficulty of generating test cases to detect such faults present significant challenges in software testing. To resolve these challenges, this paper suggests a novel method that integrates mutat...
This article discusses a variety of important notions, including ideal convergence and ideal Cauchyness of topological sequences produced by fuzzy normed spaces. Furthermore, the connections between the concepts of the ideal limit and ideal cluster p...
Transit-oriented development (TOD) pursues sustainable urban development through compact growth, mixed-use zoning, and pedestrian-friendly neighborhood design in cooperation with transportation planning. Seoul has actively developed urban rail transi...
Building clustering is a key challenge in cartographic generalization, where the goal is to group spatially related buildings into semantically coherent clusters while preserving the true distribution patterns of urban structures. Existing methods of...
We present a comprehensive study of host galaxies of radio sources within the 1.35
This paper presents an extended study about the compression of topological models of indoor environments. The performance of two clustering methods is tested in order to know their utility both to build a model of the environment and to solve the loc...
The growing penetration of distributed photovoltaic (PV) systems presents significant operational challenges for power grids, driven by the scarcity of historical data and the high spatiotemporal variability of PV generation. To address these challen...
Two well-known drawbacks in fuzzy clustering are the requirement of assigning in advance the number of clusters and random initialization of cluster centers. The quality of the final fuzzy clusters depends heavily on the initial choice of the number...
A modified method for better superpixel generation based on simple linear iterative clustering (SLIC) is presented and named BSLIC in this paper. By initializing cluster centers in hexagon distribution and performing k-means clustering in a limited r...
Object segmentation algorithms have heavily relied on deep learning techniques to estimate the count of grapes which is a strong indicator for the yield success of grapes. The issue with using object segmentation algorithms for grape analytics is tha...
Cluster validity indices (CVIs) for evaluating the result of the optimal number of clusters are critical measures in clustering problems. Most CVIs are designed for typical data-type objects called certain data objects. Certain data objects only have...
As a result of rapid urbanization in China, the spatial restructuring of towns and cities has significantly impacted urban compactness. The study of the spatio–temporal characteristics and driving mechanisms of urban compactness in central Chin...
Successful quality control and quality assurance (QC/QA) of earthwork compaction is critical to the long-term performance of roads, railways, airports, dams, and embankments. The purpose of this paper is to provide insights into the current practice,...
In this study, biometrics assessment of flower structure, cluster-, and berry-related traits were evaluated in a population of 90 muscadine grape genotypes for three consecutive years. This population consisted of 21 standard cultivars, 60 breeding l...
The Thompson Seedless (Sultanina) seedless variety of grapes is an important crop in Egypt, both for local consumption and export. In recent years, the H4 strain of this grape variety has gained popularity due to its high productivity. However, a dra...
The orbital evolution of a binary system consisting of two primordial black hole clusters is investigated. Such clusters are predicted in some theoretical models with broken symmetry in the inflation Lagrangian. A cluster consists of the most massive...
This study tested 15 direct compaction grades to identify the contribution of different grades of mannitol to the storage stability of the resulting tablets. After preparing the model tablets with different values of hardness, they were stored at 25...
Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the hea...
Aiming at the problems of existing time series data clustering methods, such as the lack of similarity metric universality, the influence of dimensional catastrophe, and the limitation of feature expression ability, a time series data clustering meth...
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