Hachemi Bennaceur, Meznah Almutairy, Norah Alhussain^*

Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2687-2706, 2023, DOI:10.32604/iasc.2023.038723

Abstract The dimensionality of data is increasing very rapidly, which creates challenges for most of the current mining and learning algorithms, such as large memory requirements and high computational costs. The literature includes much research on feature selection for supervised learning. However, feature selection for unsupervised learning has only recently been studied. Finding the subset of features in unsupervised learning that enhances the performance is challenging since the clusters are indeterminate. This work proposes a hybrid technique for unsupervised feature selection called GAk-MEANS, which combines the genetic algorithm (GA) approach with the classical k-Means algorithm. In the proposed algorithm, a new… More >

I. Vallirathi^1,*, S. Ebenezer Juliet²

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2349-2360, 2023, DOI:10.32604/csse.2023.024864

Abstract Wireless Sensor Network (WSNs) is an infrastructure-less wireless network deployed in an increasing number of wireless sensors in an ad-hoc manner. As the sensor nodes could be powered using batteries, the development of WSN energy constraints is considered to be a key issue. In wireless sensor networks (WSNs), wireless mobile chargers (MCs) conquer such issues mainly, energy shortages. The proposed work is to produce an energy-efficient recharge method for Wireless Rechargeable Sensor Network (WRSN), which results in a longer lifespan of the network by reducing charging delay and maintaining the residual energy of the sensor. In this algorithm, each node… More >

Ayman Altameem¹, Ramesh Chandra Poonia², Ankit Kumar³, Linesh Raja⁴, Abdul Khader Jilani Saudagar^5,*

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 553-566, 2023, DOI:10.32604/iasc.2023.027579

Abstract Data clustering is crucial when it comes to data processing and analytics. The new clustering method overcomes the challenge of evaluating and extracting data from big data. Numerical or categorical data can be grouped. Existing clustering methods favor numerical data clustering and ignore categorical data clustering. Until recently, the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods. However, these algorithms could not use the concept of categorical data for clustering. Following that, suggestions for expanding traditional categorical data processing methods were made. In addition to… More >

R. Gopi^1,*, S. Veena², S. Balasubramanian³, D. Ramya⁴, P. Ilanchezhian⁵, A. Harshavardhan⁶, Zatin Gupta⁷

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1215-1230, 2022, DOI:10.32604/iasc.2022.025792

Abstract In this modern era, the transformation of conventional objects into smart ones via internet vitality, data management, together with many more are the main aim of the Internet of Things (IoT) centered Big Data (BD) analysis. In the past few years, significant augmentation in the IoT-centered Healthcare (HC) monitoring can be seen. Nevertheless, the merging of health-specific parameters along with IoT-centric Health Monitoring (HM) systems with BD handling ability is turned out to be a complicated research scope. With the aid of Map-Reduce and LSQN³ techniques, this paper proposed IoT devices in Wireless Sensors Networks (WSN) centered BD Mining (BDM)… More >

Farzaneh Khorasani¹, Morteza Mohammadi Zanjireh^1,*, Mahdi Bahaghighat¹, Qin Xin²

Computer Systems Science and Engineering, Vol.40, No.3, pp. 1085-1098, 2022, DOI:10.32604/csse.2022.016003

Abstract With a sharp increase in the information volume, analyzing and retrieving this vast data volume is much more essential than ever. One of the main techniques that would be beneficial in this regard is called the Clustering method. Clustering aims to classify objects so that all objects within a cluster have similar features while other objects in different clusters are as distinct as possible. One of the most widely used clustering algorithms with the well and approved performance in different applications is the k-means algorithm. The main problem of the k-means algorithm is its performance which can be directly affected… More >

Manoj Sindhwani¹, Charanjeet Singh^1,*, Rajeshwar Singh²

Computer Systems Science and Engineering, Vol.40, No.2, pp. 455-467, 2022, DOI:10.32604/csse.2022.018498

Abstract With the growth of Vehicular Ad-hoc Networks, many services delivery is gaining more attention from the intelligent transportation system. However, mobility characteristics of vehicular networks cause frequent disconnection of routes, especially during the delivery of data. In both developed and developing countries, a lot of time is consumed due to traffic congestion. This has significant negative consequences, including driver stress due to increased time demand, decreased productivity for various personalized and commercial vehicles, and increased emissions of hazardous gases especially air polluting gases are impacting public health in highly populated areas. Clustering is one of the most powerful strategies for… More >

Hang He^1,*, Zhenhan Zhao¹, Weiwei Luo¹, Jinghui Zhang²

Computer Systems Science and Engineering, Vol.39, No.2, pp. 251-264, 2021, DOI:10.32604/csse.2021.017296

Abstract With the increasing number of airports and the expansion of their scale, the aviation network has become complex and hierarchical. In order to investigate the complex network characteristics of aviation networks, this paper constructs a Chinese aviation network model and carries out related research based on complex network theory and K-means algorithm. Initially, the P-space model is employed to construct the Chinese aviation network model. Then, complex network indicators such as degree, clustering coefficient, average path length, betweenness and coreness are selected to investigate the complex characteristics and hierarchical features of aviation networks and explore their causes. Secondly, using K-means… More >

SeaYoung Park¹, Jong-Yong Lee², Daesung Lee^3,*

CMC-Computers, Materials & Continua, Vol.67, No.3, pp. 4019-4037, 2021, DOI:10.32604/cmc.2021.014353

Abstract Replacing or recharging batteries in the sensor nodes of a wireless sensor network (WSN) is a significant challenge. Therefore, efficient power utilization by sensors is a critical requirement, and it is closely related to the life span of the network. Once a sensor node consumes all its energy, it will no longer function properly. Therefore, various protocols have been proposed to minimize the energy consumption of sensors and thus prolong the network operation. Recently, clustering algorithms combined with artificial intelligence have been proposed for this purpose. In particular, various protocols employ the K-means clustering algorithm, which is a machine learning… More >

Ahmed A. M. Jamel^1,∗, Bahriye Akay^2,†

Computer Systems Science and Engineering, Vol.35, No.6, pp. 441-456, 2020, DOI:10.32604/csse.2020.35.441

Abstract Recently, owing to the capability of mobile and wearable devices to sense daily human activity, human activity recognition (HAR) datasets have become a large-scale data resource. Due to the heterogeneity and nonlinearly separable nature of the data recorded by these sensors, the datasets generated require special techniques to accurately predict human activity and mitigate the considerable heterogeneity. Consequently, classic clustering algorithms do not work well with these data. Hence, kernelization, which converts the data into a new feature vector representation, is performed on nonlinearly separable data. This study aims to present a robust method to perform HAR data clustering to… More >

Imed Kacem, Ahmed Kadri, Pierre Laroche

Intelligent Automation & Soft Computing, Vol.24, No.2, pp. 421-430, 2018, DOI:10.31209/2018.100000016

Abstract This paper addresses an inventory regulation problem in bicycle sharingsystems. The problem is to balance a network consisting of a set of stations by using a single vehicle, with the aim of minimizing the weighted sum of the waiting times during which some stations remain imbalanced. Motivated by the complexity of this problem, we propose a two-stage procedure based on decomposition. First, the network is divided into multiple zones by using two different clustering strategies. Then, the balancing problem is solved in each zone. Finally, the order in which the zones must be visited is defined. To solve these problems,… More >