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Article | 01-December-2016

UNSUPERVISED GROUPING OF MOVING OBJECTS BASED ON AGGLOMERATIVE HIERARCHICAL CLUSTERING

In this article, we present a method to identify a grouping of sensor nodes that show similar movement patterns in an ad-hoc manner. The motivation behind the ad-hoc grouping is to allow a system to monitor complex and concrete situations of people and/or devices such as “who is/are utilizing what object(s)” and “what objects are carried together” without any supervision of human before and at the time of interaction. An agglomerative hierarchical clustering algorithm was applied to a data

Kaori Fujinami

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 2276–2296

Article | 16-December-2013

A NEW UNEQUAL CLUSTERING ALGORITHM USING ENERGY-BALANCED AREA PARTITIONING FOR WIRELESS SENSOR NETWORKS

Multi-hop Clustering is more preferred than the single-hop one because the transmission range of the Wireless Sensor Networks (WSN) nodes is not determined by the size of the network area. However, the multi-hop method brings a new problem related to the workload imbalance between the cluster-heads (CHs). Unequal Clustering becomes the most proposed solution of the unbalance load, but the calculation of the radius clusters still become a big challenge to achieve a maximum balancing degree. In

Amin Suharjono, Wirawan, Gamantyo Hendrantoro

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 5, 1808–1829

Article | 01-September-2015

COMPUTER VISION-BASED COLOR IMAGE SEGMENTATION WITH IMPROVED KERNEL CLUSTERING

Color image segmentation has been widely applied to diverse fields in the past decades for containing more information than gray ones, whose essence is a process of clustering according to the color of pixels. However, traditional clustering methods do not scale well with the number of data, which limits the ability of handling massive data effectively. We developed an improved kernel clustering algorithm for computing the different clusters of given color images in kernel-induced space for

Yongqing Wang, Chunxiang Wang

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1706–1729

Article | 01-December-2014

FACE RECOGNITION BASED ON IMPROVED SUPPORT VECTOR CLUSTERING

Traditional methods for face recognition do not scale well with the number of training sample, which limits the wide applications of related techniques. We propose an improved Support Vector Clustering algorithm to handle the large-scale biometric feature data effectively. We prove theoretically that the proposed algorithm converges to the optimum within any given precision quickly. Compared to related state-of-the-art Support Vector Clustering algorithms, it has the competitive performances on

Yongqing Wang, Xiling Liu

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1807–1829

Research Article | 27-February-2017

IDENTIFYING WAREHOUSE LOCATION USING HIERARCHICAL CLUSTERING

Summary. Identifying the optimal warehouse location involves a series of qualitative and quantitative factors. The purpose of this study was to use hierarchical clustering to identify the optimal location for a warehouse, which would ensure the lowest cost, a high level of quality in supplying customers and connect the selling and purchasing activities of the businesses operating in the Slovenian automotive industry into a system. The study also aims to demonstrate the applicability of the

Sebastjan ŠKERLIČ, Robert MUHA

Transport Problems, Volume 11 , ISSUE 3, 121–129

research-article | 30-November-2020

Energy-Aware Clustering and Efficient Cluster Head Selection

batteries that are not generally rechargeable, when the battery of node dies a node is no longer useful (Sheta and Solaiman, 2015). Implying a smart routing protocol to efficiently utilize the battery resource can contribute in prolong the network lifetime. This is the reason why researchers are now days developing new routing algorithms for WSNs to save energy and improve QoS parameters like network lifetime, delay, and throughput. Some of the protocols use a clustering approach, the technique of

EL IDRISSI Nezha, Najid Abdellah, El Alami Hassan

International Journal on Smart Sensing and Intelligent Systems, Volume 14 , ISSUE 1, 1–15

Article | 23-April-2018

Research on Balanced Energy Consumption of Wireless Sensor Network Nodes Based on Clustering Algorithm

A multi-cluster-head based clustering routing algorithm is researched and realized in order to achieve better balance the energy consumption of wireless sensor network nodes as well as promote the stability and extend the service life of the network. By taking cluster as the basic unit, it divides the wireless sensor network into multiple clusters, each of which includes a main cluster head node, an assistant cluster head node, a cluster management node and several ordinary nodes. The article

Jie Huang

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 15–19

Article | 01-December-2016

SPECTRAL CLUSTERING WITH SPATIAL COHERENCE PROPERTY JOINTING TO ACTIVE CONTOUR MODEL FOR IMAGE LOCAL SE GMENTATION

Local Segmentation is the fundamental task for image processing. Consider to the problem of low segmentation precision and contour control instability for image local segmentation, a local segmentation theory is researched that based on SSCACM (spectral clustering with spatial coherence property jointing active contour model). First, by applying spatial coherence property constraint of image pixels to spectral clustering, an adaptive similarity function is constructed and the corresponding

Guang Hu, Shengzhi Yuan

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 4, 1731–1749

Article | 10-April-2018

An Efficient Density-Based Clustering Algorithm for the Capacitated Vehicle Routing Problem

The capacitated vehicle routing problem (CVRP) is one of the most challenging problems in the optimization of distribution. Most approaches can solve case studies involving less than 100 nodes to optimality, but time-consuming. To overcome the limitation, this paper presents a novel two-phase heuristic approach for the capacitated vehicle routing problem. Phase I aims to identifying sets of cost-effective feasible clusters through an improved density-based clustering algorithm. Phase II assigns

Jiashan Zhang

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 161–165

Article | 02-April-2018

A Disparateness-Aware Scheduling using K-Centroids Clustering and PSO Techniques in Hadoop Cluster

cluster environment. In this research work we represent K-centroids clustering in big data mechanism for Hadoop cluster. This approach is mainly focused on the energy consumption in The Hadoop cluster, which helps to increase the system reliability. The Hadoop cluster consists of resources which are categorized for minimizing the scheduling delay in the Hadoop cluster using the K-Centroids clustering algorithm. A novel provisioning mechanism is introduced along with the consideration of load, energy

E. Laxmi Lydia, M.Ben Swarup

International Journal of Advanced Network, Monitoring and Controls, Volume 1 , ISSUE 2, 34–46

Research paper | 01-September-2014

POSTERIOR BELIEF CLUSTERING ALGORITHM FOR ENERGY-EFFICIENT TRACKING IN WIRELESS SENSOR NETWORKS

In this paper, we propose a novel posterior belief clustering (PBC) algorithm to solve the tradeoff between target tracking performance and sensors energy consumption in wireless sensor networks. We model the target tracking under dynamic uncertain environment using partially observable Markov decision processes (POMDPs), and transform the optimization of the tradeoff between tracking performance and energy consumption into yielding the optimal value function of POMDPs. We analyze the error of

Bo Wu, Yanpeng Feng, Hongyan Zheng

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 628–641

research-article | 28-May-2021

TDMA-clustering-based approach to avoid the reader-to-tag collision problem during the stocktaking process

that uses the TDMA scheme and the clustering approach to permit communications between interfering readers. In so doing, we developed a communication protocol to be implemented in the network’s cluster heads to provide the readers’ synchronization and coordinate their communications. To avoid collisions between adjacent clusters, we added collaboration functionality to the designed protocol that allows scan synchronization between adjacent clusters. The rest of the paper is organized as follows. In

Sara El Ouadaa, Slimane Bah, Abdelaziz Berrado

International Journal on Smart Sensing and Intelligent Systems, Volume 14 , ISSUE 1, 1–14

Article | 08-April-2018

Improved K-means Algorithm Based on optimizing Initial Cluster Centers and Its Application

Data mining is a process of data grouping or partitioning from the large and complex data, and the clustering analysis is an important research field in data mining. The K-means algorithm is considered to be the most important unsupervised machine learning method in clustering, which can divide all the data into k subclasses that are very different from each other. By constantly iterating, the distance between each data object and the center of its subclass is minimized. Because K-means

Xue Linyao, Wang Jianguo

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 2, 9–16

Article | 22-July-2019

THE EFFECT OF BINARY DATA TRANSFORMATION IN CATEGORICAL DATA CLUSTERING

This paper focuses on hierarchical clustering of categorical data and compares two approaches which can be used for this task. The first one, an extremely common approach, is to perform a binary transformation of the categorical variables into sets of dummy variables and then use the similarity measures suited for binary data. These similarity measures are well examined, and they occur in both commercial and non-commercial software. However, a binary transformation can possibly cause a loss of

Jana Cibulková, Zdenek Šulc, Sergej Sirota, Hana Rezanková

Statistics in Transition New Series, Volume 20 , ISSUE 2, 33–47

Research Article | 27-December-2017

AN ARTIFICIAL IMMUNE NETWORK CLUSTERING ALGORITHM FOR MANGROVES REMOTE SENSING IMAGE

paper presents an immune network based unsupervised classification method, which is not necessary to define complex objective function. By arbitrarily selecting a certain number of data to be training samples, the proposed algorithm mines the prior knowledge of the samples and selects a few samples to constitute the initial nodes of immune network. After the evolutionary of the immune network, the clustering results are obtained, combined with nearest neighbor classification mechanism, the

Yanmin LUO, Peizhong LIU, Minghong LIAO

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 1, 116–134

Research paper | 01-June-2011

A Novel Biased Energy Distribution (BED) Technique for Cluster-Based Routing in Wireless Sensor Networks

This paper presents the impact of utilizing a biased energy distribution (BED) scheme for clustering sensor networks. In clustering sensor networks, some of the nodes are elected as aggregators and they compress the data from their cluster members before sending the aggregated data to the sink. Existing clustering routing protocols assume that all the nodes are provided with equal amount of energy but this shortens the network lifetime and makes the network unstable. This paper proposes a

A. F. Salami, H. Bello-Salau, F. Anwar, A. M. Aibinu

International Journal on Smart Sensing and Intelligent Systems, Volume 4 , ISSUE 2, 161–173

Article | 30-November-2018

Application of K-means Algorithm in Geological Disaster Monitoring System

well. K-means algorithm is a clustering algorithm based on the classification of the classic algorithm, the algorithm in the industrial and commercial applications more widely. As we all know, it both has many advantages and many disadvantages. In this paper, we mainly study the optimization of the initial clustering center and the avoidance of the blindness of the k-value selection, and propose the CMU-kmeans algorithm. The data source of the study is the historical data detected by the geological

Wang Jianguo, Xue Linyao

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 3, 16–22

Article | 06-July-2017

IN SEARCH OF HEDGES AND SAFE HAVENS IN GLOBAL FINANCIAL MARKETS

by statistical clustering techniques; however, only conditional variances (without conditional covariances) will be taken into account. The reason for this assumption is connected with the fact that variances can be understood as market risk, and, as such, are a good indicator of market conditions. A considerable advantage of such an approach is the lack of need to determine the number of market regimes, as it is established by clustering quality measures. What is more, the methodology used in

Stanisław Wanat, Sławomir Śmiech, Monika Papież

Statistics in Transition New Series, Volume 17 , ISSUE 3, 557–574

Article | 01-September-2015

MODELLING OF EQUIPMENT FAILURE RATE ACCOUNTING FOR THE UNCERTAINTY

A fuzzy model for failure rate with the consideration of the effects of uncertain factors in distribution reliability evaluation is presented. The possibility and credibility distribution analyzed on the basis of sample datum are used for quantifying effects of the uncertainty done to failure rate. Mathematically, the failure rate can be obtained in the interval integration. Moreover, aiming to make the calculating quantity of system reliability evaluation simple and easy, the fuzzy clustering

H.X. Tian, W.F Wu, P. Wang, H.Z. Li

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1484–1504

Research Article | 27-December-2017

NEW INTELLIGENT CLASSIFICATION METHOD BASED ON IMPROVED MEB ALGORITHM

. We also use the proposed algorithm to handle Binary Classification, Multi-class Classification, and Image Clustering problems. Experiments on both synthetic and real-world data sets demonstrate the validity of the algorithm we proposed.

Yongqing Wang, Lei Liu

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 1, 72–95

Research Article | 13-December-2017

COOPERATIVE MULTI TARGET TRACKING USING MULTI SENSOR NETWORK

tracking system using multiple mobile sensors. For the purposes of surveillance and security, trackers use an Extended Kohonen neural network to track the moving targets in their environments. The proposed tracking algorithm can be used for single and multiple target tracking. A clustering algorithm is used in order to minimize the number of active trackers over time and hence save energy. An auction based algorithm is used for the purpose of optimizing the cooperation between trackers. Quantitative

Ahmed M. Elmogy, Fakhreddine O. Karray

International Journal on Smart Sensing and Intelligent Systems, Volume 1 , ISSUE 3, 716–734

Article | 07-May-2018

Design and Implementation of Music Recommendation System Based on Hadoop

, database and ETL operation, which can calculate a set of complete recommendation system from user operation end to server and data calculation. In order to improve the accuracy of the recommendation algorithm, this paper introduces k-means clustering algorithm to improve the recommendation algorithm based on user-based collaborative filtering.The experimental results show that the accuracy of the proposed algorithm has been significantly improved after the introduction of k-means.

Zhao Yufeng, Li Xinwei

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 2, 126–132

Article | 30-November-2018

Hazard Grading Model of Terrorist Attack Based on Machine Learning

management of social security, but also avoid unnecessary waste of manpower and property. Combined with big data processing technology, this paper establishes a hierarchical model based on PCA algorithm, K-meas clustering algorithm and entropy method. First, 14 evaluation indicators related to the hazard of the event were selected to preprocess the existing data. Secondly, the PCA method was used to reduce the index from 14 dimensions to 4 dimensions, and the reduced dimension vector was obtained by the

Jun Yu, Tong Xian, Zhiyi Hu, Yutong Liu

International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 2, 81–85

Article | 13-July-2020

Improved Faster R-CNN Algorithm for Sea Object Detection Under Complex Sea Conditions

detection accuracy of the above method is not high. In this paper, the Faster R-CNN[11-13] object detection algorithm is improved. The K-Means clustering algorithm[14] is introduced to perform cluster analysis on the size of the object in the image, and the clustering results are directly input into the area recommendation network to achieve the improvement of the area recommendation network. Using Soft -The NMS algorithm replaces the NMS algorithm to reduce the miss detection probability of small

Liu Yabin, Yu Jun, Hu Zhiyi

International Journal of Advanced Network, Monitoring and Controls, Volume 5 , ISSUE 2, 76–82

Article | 30-November-2018

Detection of Blink State Based on Fatigued Driving

or inaccurate positioning[7]. Therefore, this paper chooses Dlib open source library to detect human eye features. Firstly, the 68 face feature points provided by the Dlib open source library are used to accurately calibrate the position of the face and the human eye, and then the aspect ratio between the length and the width of the human eye is measured. Finally, the Kmeans clustering algorithm is used to analyze the collected ratio. The threshold of blinking. Figure 1 below, a is the 68 face

Lei Chao, Wang Changyuan, Li Guang, Shi Lu

International Journal of Advanced Network, Monitoring and Controls, Volume 4 , ISSUE 4, 24–29

Research Article | 15-February-2020

A Fuzzy Based Predictive Cluster Head Selection Scheme for Wireless Sensor Networks

Clustering, an energy efficient approach is used in Wireless Sensor Network. Clustering involves cluster formation and Cluster Head Selection. As the Cluster Head is involved in carrying out the entire communication, a high energy node has to be selected as Cluster Head. In this paper, a novel predictive Fuzzy based Cluster Head selection algorithm is proposed. The proposal suggests a new input parameter, Rate of recurrent Communication apart from the standard parameters namely the Residual

Hemavathi Natarajan, Sudha Selvara

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–8

Article | 05-September-2021

An application of persistent homology and the graph theory to linguistics: The case of Tifinagh and Phoenician scripts

As the origin of the Tifinagh script remains uncertain, this work aims at exploring its proba ble relatedness with the Phoenician script. Using tools from within topological data analysis and graph theory, the similarity between the two scripts is studied. The clustering of their letter shapes is performed based on the pairwise distances between their topological signa tures. The ideas presented in this work can be extended to study the similarity between any two writing systems and as such can

Hajar Bouazzaoui, Mohamed Abdou Elomary, My Ismail Mamouni

Statistics in Transition New Series, Volume 22 , ISSUE 3, 141–156

Research Article | 01-September-2017

A MULTI-KEYWORD RANKED SEARCH SCHEME OVER ENCRYPTED BASED ON HIERARCHICAL CLUSTERING INDEX

A. Indhuja, T.P.Udaya shankar, R.M Balajee Mastan Vali Shaik, P Sujatha

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 539–559

research-article | 24-December-2019

Ukrainian and Russian organizations in Sweden and the conflict “back home”

different ethnic organizations build with each other. Thus, shared ethnicity can lose its importance in how an organization decides to form a connection with another, and shared attitudes about the conflict can become a leading mechanism in forming collaborations. In other words, an ethnically homophilous collaboration network may reorient itself into clustering by attitudes toward the conflict, and thus actively choose to become homophilious based on that perceived value. An organization can, therefore

Sofiya Voytiv

Connections: The Quarterly Journal, Volume 39 , ISSUE 1, 1–20

research-article | 30-November-2020

Characterization of Globodera ellingtonae Populations from Chile Utilizing Whole Genome Sequencing

included in the analyses. Whole-genome shotgun sequencing followed by genetic variant calling of individual cysts revealed a population structure broadly correlated with geographic region. A phylogenetic tree derived from 168,354 binary SNPs in the nuclear genome showed separate but distinct clustering of the INRAE Antofagasta and Oregon G. ellingtonae populations (Fig. 2). Seven cysts from Socaire formed a well-supported clade, as did cysts from Talabre which formed a supported clade along with two

C.N. Hesse, I. Moreno, O. Acevedo Pardo, H. Pacheco Fuentes, E. Grenier, L. M. Dandurand, I. A. Zasada

Journal of Nematology, Volume 53 , 1–9

Article | 31-December-2020

DRIVING STYLE ANALYSIS AND DRIVER CLASSIFICATION USING OBD DATA OF A HYBRID ELECTRIC VEHICLE

and ecological safety. Relations between the type of driver, driving dynamics, and fuel consumption were studied. The driver's categorization was based on a statistical analysis of input signals and mean tractive force (MTF) by clustering.

Andrzej PUCHALSKI, Iwona KOMORSKA

Transport Problems, Volume 15 , ISSUE 4, Part 1, 83–94

Research Article | 20-February-2013

OUTLIER DETECTION BASED ON SIMILAR FLOCKING MODEL IN WIRELESS SENSOR NETWORKS

Cheng Chunling, Wu Hao, Yu Zhihu, Zhang Dengyin, Xu Xiaolong

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 18–37

Research Article | 20-February-2013

CFM: A FITNESS-MODEL-BASED TOPOLOGY CONTROL ALGORITHM FOR WIRELESS SENSOR NETWORKS

topology can be achieved by fitness model, so we designed an approximate clustering algorithm based on fitness model, which is distributed. CFM is composed of three phases: links generation phase, heads selection phase and cluster division phase. The performance of CFM algorithm was analyzed through simulation experiments, which indicated a well-constructed topology and effectively prolonged network lifetime.

Linfeng Liu, Jiagao Wu, Fu Xiao, Ruchuan Wang

International Journal on Smart Sensing and Intelligent Systems, Volume 6 , ISSUE 1, 58–76

Article | 01-September-2014

EMPIRICAL MODE DECOMPOSITION AND ROUGH SET ATTRIBUTE REDUCTION FOR ULTRASONIC FLAW SIGNAL CLASSIFICATION

Feature extraction and selection are the most important techniques for ultrasonic flaw signal classification. In this study, empirical mode decomposition (EMD) is first used to obtain the intrinsic mode functions (IMFs) of original ultrasonic signals. Such IMFs and traditional time as well as frequency domain based statistical parameters are extracted as the initial features of flaw signal. After that, spectral clustering method is used for feature value discretization so that rough set

Yu Wang

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 1401–1420

Article | 01-December-2014

A SCENE RECOGNITION ALGORITHM BASED ON MULTI-INSTANCE LEARNING

In Bag of Words image presentation model, visual words are generated by unsupervised clustering, which leaves out the spatial relations between words and results in such shorting comings as limited semantic description and weak discrimination. To solve this problem, we propose to substitute visual words by visual phrases in this article. Visual phrases built according to spatial relations between words are semantic distrainable, and they can improve the accuracy of Bag of Words model

Tao Wang, Wenqing Chen, Bailing Wang

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1470–1492

Article | 01-December-2014

RESEARCH OF MULTI SENSOR INTELLIGENT SYSTEM SIGNAL FUSION AND RECONSTRUCTION

computation based on. In order to further reduce the sampling effort of large quantities of multifunctional sensor signal reconstruction, is proposed based on the reduction method clustering multifunctional sensor sample selection method, to select the reasonable distribution, suitable for inverse training data model. A new direction to study the theory of multi sensor information fusion is to design and analysis more efficient processing of multi sensor intelligent system is proposed and developed. With

Junjie Yang, Wenxiang Chen, Zhihe Fu, Wei Wu, Zhiping Xie

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 4, 1701–1716

Article | 01-September-2015

FRAGRANCE MEASUREMENT OF SCENTED RICE USING ELECTRONIC NOSE

into a sensor chamber; a water bath module for preparing rice sample, said water bath module including a heater attachment to facilitate cooking; a computing module to quantify the aroma data acquired by sensors; data acquisition module etc. Principal Component Analysis (PCA) implemented for clustering the data sets acquired from sensor array. Also data generated from sensor array was fed to Probabilistic Neural Network (PNN), Back-propagation Multilayer Perceptron (BPMLP) and Linear Discriminant

Arun Jana, Nabarun Bhattacharyya, Rajib Bandyopadhyay, Bipan Tudu, Subhankar Mukherjee, Devdulal Ghosh, Jayanta Kumar Roy

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 3, 1730–1747

Article | 11-March-2018

The Structure of Collaboration Networks: An Illustration of Indian Economics

following social network measures are applied: the size of the network, the size of the main component, average degree, path length, and clustering coefficient. The study presents the following three features of Indian economics: first, a substantial proportion of Indian authors are isolated, albeit declining very slowly over a period of time; second, it appears that the structure of scholarly collaboration in Indian economics is highly fragmented, and the observed size of main components accounts for a

M. Krishna, G.D. Bino Paul

Journal of Social Structure, Volume 18 , ISSUE 1, –

research-article | 30-November-2018

Mapping Gray Maritime Networks

network analytics (e.g., eigenvector centrality, density, clustering, cohesiveness/structural holes) not possible with link analysis (Granovetter, 1973; Watts, 2004; Kadushin, 2012; Prell, 2012; Borgatti et al., 2013; Freeman, 2016). This research explored how SNA and open source data integration from existing databases may be applied to any nefarious (gray or dark) maritime network, providing the ability to geo-locate and track stakeholders and nodes in these networks in physical and virtual space

Wayne Porter, Rob Schroeder, Chris Callaghan, Albert Barreto, Sam Bussell, Brian Young, Manuel Loewer, Daniel Funk, Janet von Eiff

Connections: The Quarterly Journal, Volume 39 , ISSUE 1, 1–12

research-article | 31-August-2021

Road Obstacle Object Detection Based on Improved YOLO V4

speed, it still has some shortcomings, such as the random initial clustering center of the anchor box a priori box generated by the Kmeans method, resulting in inaccurate clustering results; The too high coincidence of urban environment makes it difficult to predict coordinates, which leads to the low accuracy of the detection results. In order to solve such problems, this paper proposes an improved YOLO V4 object detection algorithm. By improving the network structure of the algorithm, improving

Xiao Zuo, Jun Yu, Tong Xian, Yuzhe Hu, Zhiyi Hu

International Journal of Advanced Network, Monitoring and Controls, Volume 6 , ISSUE 3, 18–25

Research Article | 01-September-2017

CLUSTER HEAD ELECTION MECHANISM-BASED ON FUZZY LOGIC (CHEF) WITH TDMA IN WSN

paper proposes the mechanism or device is capable of utilizing its own system of control simply called as self-configurable clustering mechanism to detect the disordered CHs and replace them with other nodes. And results have been derived from simulator ns-2 to show the better performance.

G Vijayalakshmi, M.Anto Bennet, P. Shenbagavalli, M. Vijayalakshmi, S. Saranya

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 395–413

Research Article | 15-February-2020

A Proposal for Classification of Multisensor Time Series Data based on Time Delay Embedding

Multisensor time series data is common in many applications of process industry, medical and health care, biometrics etc.Analysis of multisensor time series data requires analysis of multidimensional time series(MTS) which is challenging as they constitute a huge volume of data of dynamic nature. Traditional machine learning algorithms for classification and clustering developed for static data can not be applied directly to MTS data. Various techniques have been developed to represent MTS data

Basabi Chakraborty

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 5, 1–5

Article | 01-December-2015

SEFP: A NEW ROUTING APPROACH USING FUZZY LOGIC FOR CLUSTERED HETEROGENEOUS WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) are composed of set sensor nodes communicating through wireless channels with limited resources. Therefore, several routing protocols and approaches about energy efficient operation of WSNs have been proposed. Clustering algorithm based routing protocols are well used for efficient management of sensing sensor node energy resources. However, many researches were focused on optimization of well-known hierarchical routing approaches of WSNs using fuzzy logic system

hassan EL ALAMI, Abdellah NAJID

International Journal on Smart Sensing and Intelligent Systems, Volume 8 , ISSUE 4, 2286–2306

Article | 01-June-2016

TRUST AWARE DATA AGGREGATION AND INTRUSION DETECTION SYSTEM FOR WIRELESS SENSOR NETWORKS

. On the other hand, the Base Station (BS) is equipped with IDS that collects the trust values of network nodes and runs a statistical test to identify malicious nodes. In this way, TMS at the node level and IDS at BS level work in collaboration to detect and isolate the malicious nodes from the DA process. The simulation results show the effectiveness of the TDAGIDS over baseline T-LEACH (Trusted Low Energy Adaptive Clustering Hierarchy) protocol and recently proposed TDAGIOT (Trust based Data

P. Raghu Vamsi, Krishna Kant

International Journal on Smart Sensing and Intelligent Systems, Volume 9 , ISSUE 2, 537–562

Article | 03-December-2017

Morphological, Morphometric, and Molecular Characterization of Intraspecific Variations within Indian Populations of Meloidogyne graminicola

RAJAN SALALIA, R. K. WALIA, VISHAL SINGH SOMVANSHI, PUNEET KUMAR, ANIL KUMAR

Journal of Nematology, Volume 49 , ISSUE 3, 254–267

Research Article | 03-March-2021

A latent class analysis on the usage of mobile phones among management students

are most useful for the clustering of respondents into different classes. The findings demonstrate that young people display various feelings and attitudes toward cell phone usage.

Sunil Kumar, Apurba Vishal Dabgotra

Statistics in Transition New Series, Volume 22 , ISSUE 1, 89–114

research-article | 23-April-2020

Spatial distribution and risk area assessment of Aphelenchoides besseyi using geostatistical approaches in Giridih district of Jharkhand, India

blocks (Kaufman and Rousseeuw, 2009). Normalization of data followed by cluster analysis was performed using ‘hclust’ function in R. In average linkage hierarchical clustering, the distance (L) between two cluster (r, s) is defined as the average distance between each point of a cluster to every points of the other cluster and can be expressed as follows: (1) L ( r , s ) = 1 n r n s ∑ i = 1 n r ∑ j = 1 n s D ( x r i , x s j , (1)where

Sandip Mondal, Matiyar Rahaman Khan, Abhishek Mukherjee

Journal of Nematology, Volume 52 , 1–16

research-article | 30-November-2019

Intraspecific variation in phenotypic and phylogenetic features among Pratylenchus penetrans isolates from Wisconsin, USA

) was computed for each morphological variable by isolate and for pooled data representing all isolates. Phenotype analyses of isolates Intraspecific variation in the morphometrics among the P. penetrans isolates were evaluated with Hierarchical Agglomerative clustering (HAC) and Canonical Discriminant analysis (CDA) for the following female features: L, V%, ratios of a, b, c, c′, maximum body width, esophageal length, length of anterior end to excretory pore, lip region height, stylet length, DGO

Kanan Saikai, Ann E. MacGuidwin

Journal of Nematology, Volume 52 , 1–17

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