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Hazard Grading Model of Terrorist Attack Based on Machine Learning

Jun Yu, Tong Xian, Zhiyi Hu, Yutong Liu

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


A Comparative Study of Face Recognition Classification Algorithms

I. INTRODUCTION With the rise of artificial intelligence and machine learning, face recognition technology is widely used in life, such as station security, time and attendance punching, and secure payment [1-3], but different face recognition devices use different algorithms. Therefore, this paper analyzes and compares the commonly used classification algorithms in face recognition. The data set in this paper uses the ORL face data set published by Cambridge University in the United Kingdom

Changyuan Wang, Guang Li, Pengxiang Xue, Qiyou Wu

International Journal of Advanced Network, Monitoring and Controls , ISSUE 3, 23–29



Machine learning methods are increasingly being used to predict company bankruptcy. Comparative studies carried out on selected methods to determine their suitability for predicting company bankruptcy have demonstrated high levels of prediction accuracy for the extreme gradient boosting method in this area. This method is resistant to outliers and relieves the researcher from the burden of having to provide missing data. The aim of this study is to assess how the elimination of outliers from

Barbara Pawełek

Statistics in Transition New Series , ISSUE 2, 155–171

Research Article


are trained into the databases using machine learning algorithm. The tracking of individuals can be achieved by capturing their images while on the move and comparing them with the values stored in the databases. The detection of facial structure is done with Viola-Jones algorithm which though older is easy and efficient to use and Kanade-Lucas-Tomasi(KLT) algorithm is used for feature extraction . The HOG (Histogram of Oriented Gradients) features are extracted for training.

P.J Leo Evenss, Jennings Mcenroe .S, A.Prabhu Chakkaravarthy

International Journal on Smart Sensing and Intelligent Systems , ISSUE 5, 163–173


An Ensemble Learning Method for Text Classification Based on Heterogeneous Classifiers

Fan Huimin, Li Pengpeng, Zhao Yingze, Li Danyang

International Journal of Advanced Network, Monitoring and Controls , ISSUE 1, 130–134

Research paper


Germanas Budnikas

Statistics in Transition New Series , ISSUE 2, 309–322

Research Article

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 , ISSUE 5, 1–5


A review of privacy-preserving human and human activity recognition

As more and more data is collected and the technology to process it develops, the importance of data is growing. In addition, technology is needed for sensitive data processing to protect privacy. All processes that process data, such as raw data, data being processed, and result data, require privacy. The general approaches to prevent privacy leakage adopted anonymity, access control, and transparency (Haris et al., 2014). With the introduction of machine learning (ML), big data processing is

Im Y. Jung

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 1–13


Research on the Application of Convolutional Neural Networks in the Image Recognition

I. INTRODUCTION Since the concept of deep learning was proposed by Hinton et al[1]. In 2006, during more than a decade of development, machine learning is closer to the original goal of “artificial intelligence”. Deep learning is a hierarchical machine learning approach that involves multiple levels of nonlinear transformations that learn the inherent laws and representation levels of sample data, and the feature information obtained in the process of learning can help the machine achieve

Gao Zhiyu, Liu Bailin, Gu Hongxian, Mu Jing

International Journal of Advanced Network, Monitoring and Controls , ISSUE 2, 31–38


Traveling Route Generation Algorithm Based On LDA and Collaborative Filtering

factors, such as user’s demand, the price of route, interest arrangement, and transportation. The basic theory of route planning and generation involves multiple disciplines, including data mining, statistical machine learning, network search, pattern recognition, and spatial data mining. A scientific travel route can display as many tourist attractions and landscapes as possible to visitors, thereby improving satisfaction and happiness of tourists and promoting the long-term development of tourism

Peng Cui, Yuming Wang, Chunmei Li

International Journal of Advanced Network, Monitoring and Controls , ISSUE 4, 47–62


Application of K-means Algorithm in Geological Disaster Monitoring System

Wang Jianguo, Xue Linyao

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

Research Article


A.-M. Cretu, P. Payeur

International Journal on Smart Sensing and Intelligent Systems , ISSUE 3, 402–423

Research Article

Dwipa Ontology III: Implementation of Ontology Method Enrichment on Tourism Domain

Guson Prasamuarso Kuntarto, Irwan Prasetya Gunawan, Fahmi L. Moechtar, Yudhiansyah Ahmadin, Berkah I. Santoso

International Journal on Smart Sensing and Intelligent Systems , ISSUE 4, 903–919

Research Article

ECG Decision Support System based on feedforward Neural Networks

Abstract The success of an Electrocardiogram (ECG) Decision Support System (DSS) requires the use of an optimum machine learning approach. For this purpose, this paper investigates the use of three feedforward neural networks; the Multilayer Perceptron (MLP), the Radial Basic Function Network (RBF), and the Probabilistic Neural Network (PNN) for recognition of normal and abnormal heartbeats. Feature sets were based on ECG morphology and Discrete Wavelet Transformer (DWT) coefficients. Then, a

Hela Lassoued, Raouf Ketata, Slim Yacoub

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



into account many different factors, process large amounts of information and solve multi-criteria tasks in transport companies for all management functions. Creation of digital analytical competency for transportation systems represents a new direction of the educational and training programs that must include courses in modern assessment and forecasting methods, big data operation, machine learning, neuron networks and other approaches in artificial intelligence area. This article describes a new

Svetlana LYAPINA, Valentina TАRASOVA, Marina FEDOTOVA

Transport Problems , ISSUE 2, 71–82


A novel algorithm for estimation of Twitter users location using public available information

, given that traditional databases cannot efficiently deal with such data (Anber et al., 2016). Recently, predictive analyses of big data gained increasing attention in the computer science research society. The analysis of big data sets employs statistical methods or machine learning models to predict future results or unknown outcomes (Brown et al., 2015). Moreover, data mining techniques are used to search unstructured data based on current and historical data to predict future or unknown

Yasser Almadany, Khalid Mohammed Saffer, Ahmed K. Jameil, Saad Albawi

International Journal on Smart Sensing and Intelligent Systems , ISSUE 1, 1–10


Review of 3D Point Cloud Data Segmentation Methods

SUMMARY We divide the 3D point cloud segmentation method into:edge-based methods, region-based methods, graph-based methods, model-based methods, and machine learning-based methods based on the basis of the current segmentation. A. Edge-based methods The edge-based segmentation method is currently the most studied method[2]. Edges are the basic features that describe the shape of point cloud objects (Figure 1). The edge-based segmentation method first detects the geometric boundary points of the data

Xiaoyi Ruan, Baolong Liu

International Journal of Advanced Network, Monitoring and Controls , ISSUE 1, 66–71


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 , ISSUE 2, 9–16

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