Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has o...
Road traffic congestion has became a major problem in most countries because it affects sustainable mobility. Partitioning a transport network into homogeneous areas can be very useful for monitoring traffic as congestion is spatially correlated in a...
Customers’ electricity consumption behavior can be studied from daily load data. Studying the daily load data for user behavior pattern analysis is an emerging research area in smart grid. Traditionally, the daily load data can be clustered int...
Wireless Sensor Networks (WSNs) are key elements of Internet of Things (IoT) networks which provide sensing and wireless connectivity. Disaster management in smart cities is classified as a safety-critical application. Thus, it is important to ensure...
In this study, a new approach for novelty and anomaly detection, called HPFuzzNDA, is introduced. It is similar to the Possibilistic Fuzzy multi-class Novelty Detector (PFuzzND), which was originally developed for data streams. Both algorithms initia...
K-Means is a “de facto” standard clustering algorithm due to its simplicity and efficiency. K-Means, though, strongly depends on the initialization of the centroids (seeding method) and often gets stuck in a local sub-optimal solution. K-...
The mlr-dependent biodegradation plays an essential role in the natural attenuation of microcystins (MCs) in eutrophic freshwater ecosystems. However, their evolutionary origin is still unclear due to the lack of mlr gene cluster sequences. In this s...
Knowledge sharing behavior based on the cluster innovation network has become the primary measure for enterprises to realize sustainable innovation. In order to promote the proactive knowledge sharing behavior among enterprises in the long term, the...
2 March 2022
The innovation and development practices of the pharmaceutical industry are of great importance for the continual enhancement of public health. Industrial clusters are one of the important means by which the pharmaceutical industry can be transformed...
Cluster structure optimization (CSO) refers to finding the globally minimal cluster structure with respect to a specific model and quality criterion, and is a computationally extraordinarily hard problem. Here we report a successful hybridization of...
Evolutionary multi-objective clustering (EMOC) algorithms have gained popularity recently, as they can obtain a set of clustering solutions in a single run by optimizing multiple objectives. Particularly, in one type of EMOC algorithm, the number of...
The Internet of Things (IoT) edge is an emerging technology of sensors and devices that communicate real-time data to a network. IoT edge computing was introduced to handle the latency concerns related to cloud computing data management, as the data...
Alcohol intoxication is a significant phenomenon, affecting many social areas, including work procedures or car driving. Alcohol causes certain side effects including changing the facial thermal distribution, which may enable the contactless identifi...
In recent years, a variety of data-driven evolutionary algorithms (DDEAs) have been proposed to solve time-consuming and computationally intensive optimization problems. DDEAs are usually divided into offline DDEAs and online DDEAs, with offline DDEA...
The multi-station agricultural machinery scheduling process mainly involves two key stages: order allocation and path planning. Order allocation methods based solely on spatial distance cannot ensure the continuity of agricultural operations. Multi-o...
Previously, cluster-based multi or many objective function techniques were proposed to reduce the Pareto set. Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems. In this work,...
Nowadays, the intelligent unmanned agricultural unmanned aerial vehicle (UAV) swarm collaboration network (AUSCN) has fully demonstrated its advantages in agricultural monitoring and management. By using an AUSCN, multi-machine cooperation can be rea...
Plant chloroplast (cp) gene clusters consist of genes arranged closely together on the cp genome. These genes are organized in operon structures and participate in cotranscription, typically exhibiting conservation. Broken gene clusters have been obs...
Machine learning algorithms are used in many applications nowadays. Sometimes, we need to describe how the decision models created output, and this may not be an easy task. Information visualization (InfoVis) techniques (e.g., TreeMap, parallel coord...
As dynamic systems, social networks exhibit continuous topological changes over time, and are typically modeled as temporal networks. In order to understand their dynamic characteristics, it is essential to investigate temporal community detection (T...
Symmetric and asymmetric patterns are fascinating phenomena that show a level of co-existence in mobile application behavior analyses. For example, static phenomena, such as information sharing through collaboration with known apps, is a good example...
As data and supercomputing centres increase their performance to improve service quality and target more ambitious challenges every day, their carbon footprint also continues to grow, and has already reached the magnitude of the aviation industry. Al...
We propose a hybrid framework that integrates instance clustering with Automatic Generation of Algorithms (AGA) to produce specialized algorithms for classes of Multidimensional Knapsack Problem (MKP) instances. This approach is highly relevant given...
Numerous small and micro enterprises in Industrial clusters cannot directly participate in low-carbon technology co-innovation cooperation due to their limited technological research and development capabilities, and they need to rely on the diffusio...
Software maintenance is an important step in the software lifecycle. Software module clustering is a HHMO_CF_GDA optimization problem involving several targets that require minimization of module coupling and maximization of software cohesion. Moreov...
Modern active distribution networks are increasingly characterized by high complexity, uncertainty, and distributed clustering, posing challenges for traditional model-based simulations in capturing nonlinear dynamics and stochastic variations. This...
In this paper, we propose a latent feature group learning (LFGL) algorithm to discover the feature grouping structures and subspace clusters for high-dimensional data. The feature grouping structures, which are learned in an analytical way, can enhan...
For large-scale multi-objective optimization, it is particularly challenging for evolutionary algorithms to converge to the Pareto Front. Most existing multi-objective evolutionary algorithms (MOEAs) handle convergence and diversity in a mutually dep...
The proliferation of smart devices in the Internet of Things (IoT) networks creates significant security challenges for the communications between such devices. Blockchain is a decentralized and distributed technology that can potentially tackle the...
The growing number of Renewable Energy Sources has increased the demand for innovative and high-performing turbine designs. Due to the increase in computing resources over recent years, numerical optimization using Evolutionary Algorithms (EAs) has b...
Evolutionary Computation Methods (ECMs) are proposed as stochastic search methods to solve complex optimization problems where classical optimization methods are not suitable. Most of the proposed ECMs aim to find the global optimum for a given funct...
In the contemporary economic landscape shaped by globalization and digital transformation, patent-intensive industries have emerged as critical engines for enhancing national competitiveness. This study analyzed 98,464 collaborative patent applicatio...
Hyperspectral remote sensing images have characteristics such as high dimensionality and high redundancy. This paper proposes a pseudo-label guided artificial bee colony band selection algorithm with hypergraph clustering (HC-ABC) to remove redundant...
High Performance Computing Clusters (HPCCs) are common platforms for solving both up-to-date challenges and high-dimensional problems faced by IT service providers. Nonetheless, the use of HPCCs carries a substantial and growing economic and environm...
The paper presents a new memetic, cluster-based methodology for image registration in case of geometric perturbation model involving translation, rotation and scaling. The methodology consists of two stages. First, using the sets of the object pixels...
Species delimitation is often based on a single or very few genetic or phenetic traits, something which leads to misinterpretations and often does not provide information about evolutionary processes. Here, we investigated the diversity pattern of mu...
The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level block...
The distribution network reconfiguration problem (DNRP) refers to the challenge of searching for a given power distribution network configuration with better operating conditions, such as minimized energy losses and improved voltage levels. To accomp...
The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption dat...
Vehicular ad hoc networks (VANETs) are wireless networks of automotive nodes. Among the strategies used in VANETs to increase network connectivity are broadcast scheduling, data aggregation, and vehicular node clustering. In the context of extremely...
Disease resistance genes (R genes), as part of the plant defense system, have coevolved with corresponding pathogen molecules. The main objectives of this project were to identify non-Toll interleukin receptor, nucleotide-binding site, leucine-rich r...
Variable Mesh Optimization with Niching (VMO-N) is a framework for multimodal problems (those with multiple optima at several search subspaces). Its only two instances are restricted though. Being a potent multimodal optimizer, the Hill-Valley Evolut...
Calmodulin-binding transcription activator (CAMTA) plays an important regulatory role in plant growth, development, and stress response. This study identified the phylogenetic relationships of the CAMTA family in 42 plant species using a genome-wide...
Wireless Sensor Networks (WSNs) are very important for monitoring complex 3D environments like forests, where energy efficiency and reliable communication are critical. This paper presents EEL-GA, an Energy Efficient LEACH-based clustering protocol o...
Clustering is a widely used unsupervised learning technique across data mining and machine learning applications and finds frequent use in diverse fields ranging from astronomy, medical imaging, search and optimization, geology, geophysics, and senti...
Ergot fungi (Claviceps spp.) are infamous for producing sclerotia containing a wide spectrum of ergot alkaloids (EA) toxic to humans and animals, making them nefarious villains in the agricultural and food industries, but also treasures for pharmaceu...
Nowadays, time series data are ubiquitous, encompassing various domains like medicine, economics, energy, climate science and the Internet of Things. One crucial task in analysing these data is clustering, aiming to find patterns that indicate previo...
To fully characterize the evolution process of the topological structure of dynamic communities, we propose a dynamic community detection based on Evolutionary DeepWalk (DEDW) for the high-dimensional data and dynamic characteristics. First, DEDW sol...
In smart buildings, many different systems work in coordination to accomplish their tasks. In this process, the sensors associated with these systems collect large amounts of data generated in a streaming fashion, which is prone to concept drift. Suc...
Detecting dynamic community structure in vehicle movements is helpful for revealing urban structures and human mobility patterns. Despite the fruitful research outcomes of community detection, the discovery of irregular-shaped and statistically signi...
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