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Research Article

PREDICTIVE MICROBIOLOGY OF FOOD

The beginnings of predictive microbiology date back to 1920 when Bigelow developed a logarithmic-linear dependence of kinetics on the death of microorganisms. Predictive microbiology is a sub-discipline of food microbiology, whose task is to predict the behavior of microorganisms in food using mathematical models. The predictive model for microbiology is usually a simplified description of the correlation between the observed reactions and the factors responsible for the occurrence of these

Elżbieta Rosiak, Katarzyna Kajak-Siemaszko, Monika Trząskowska, Danuta Kołożyn-Krajewska

Postępy Mikrobiologii - Advancements of Microbiology, Volume 57 , ISSUE 3, 229–243

Research Article | 24-February-2017

DATA MINING WORKSPACE AS AN OPTIMIZATION PREDICTION TECHNIQUE FOR SOLVING TRANSPORT PROBLEMS

Summary. This article addresses the study related to forecasting with an actual high-speed decision making under careful modelling of time series data. The study uses data-mining modelling for algorithmic optimization of transport goals. Our finding brings to the future adequate techniques for the fitting of a prediction model. This model is going to be used for analyses of the future transaction costs in the frontiers of the Czech Republic. Time series prediction methods for the performance of

Anastasiia KUPTCOVA, Petr PRŮŠA, Gabriel FEDORKO, Vieroslav MOLNÁR

Transport Problems, Volume 11 , ISSUE 3, 21–31

Article | 10-April-2018

Design and Development of Intelligent Logistics System Based on Semantic Web and Data Mining Technology

The intelligent logistics distribution of e-commerce is the computer technology and modern hardware equipment, software system and advanced management tools used by the logistics distribution enterprise. Data mining technology is the process of finding the probability distribution of random variables from a large number of source data. Automation of intelligent logistics system can improve labor productivity and reduce the error of logistics operation. This paper proposes design and development

Yi Wang, Xue Bai, Haoyuan Ou

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 163–167

Article | 01-September-2014

GIS FOR ARCHEOLOGICAL DATA

The digital system of Archaeology includes multi-scale non-destructive detection (NDD) of archaeological methods, data mining technologies and the GIS of archaeological detect. Heritage preservation is not just to protect the cultural relics which have been excavated. NDD method could detect archaeological artifacts and clarify the statues of cultural relics buried underground. Due to data mining algorithm we can obtain archaeological information via detection data. Finally, apply GIS

CAO Ligang, WANG Xuben

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 1347–1363

Research Article | 01-September-2017

DATA MINING WITH BIG DATA REVOLUTION HYBRID

Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data

R. Elankavi, R. Kalaiprasath, R. Udayakumar

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 560–573

Article | 24-April-2018

Improved Statistical Analysis Method Based on Big Data Technology

Big data technology refers to the rapid acquisition of valuable information from various types of large amounts of data. It can be divided into 8 technologies: data acquisition, data access, infrastructure, data processing, statistical analysis, data mining, model prediction and results presentation. The paper presents improved statistical analysis method based on big data technology. A statistical analysis model in big data environment is designed to extract useful information features from

Hongsheng Xu, Ganglong Fan, Ke Li

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 4, 26–30

Research Article | 01-September-2017

TEMPORAL QUERY PROCESSIG USING SQL SERVER

Most data sources in real-life are not static but change their information in time. This evolution of data in time can give valuable insights to business analysts. Temporal data refers to data, where changes over time or temporal aspects play a central role. Temporal data denotes the evaluation of object characteristics over time. One of the main unresolved problems that arise during the data mining process is treating data that contains temporal information. Temporal queries on time evolving

Mastan Vali Shaik, P Sujatha

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 495–505

research-article | 30-November-2019

The Discriminant Analysis Approach for Evaluating Effectiveness of Learning in an Instructor-Led Virtual Classroom

method of teaching to improve the learners’ performance in academics. Angel et al. (2011) identified that North Carolina Virtual Public Schools (NCVPS) offered students improved flexibility and responsibilities, expanded opportunities, and individualized instruction and support. However, some problems existed for learner eagerness. Learners did not all the time have the practical skills or resources for online learning and many lacked self-direction. Han and Kamber (2006) describe the data mining

D. Magdalene Delighta Angeline, P. Ramasubramanian, I. Samuel Peter James

International Journal on Smart Sensing and Intelligent Systems, Volume 13 , ISSUE 1, 1–15

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 | 11-April-2018

The Mining Algorithm of Frequent Itemsets based on Mapreduce and FP-tree

Bo He, Jianhui Pei, Hongyuan Zhang

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 68–71

Article | 12-April-2018

Research and Application of an Intelligent Decision Support System

Xiaoqing Zhou, Zhiyong Zhou, Jianqiong Xiao, Jiaxiu Sun

International Journal of Advanced Network, Monitoring and Controls, Volume 2 , ISSUE 3, 141–144

Research Article | 27-December-2017

AN INTELLIGENT FLOOD CONTROL DECISION SUPPORT SYSTEM FOR DIGITAL URBAN MANAGEMENT

Digital Urban Management has become a trend in the development of contemporary cities. This paper presents the design and implementation of an intelligent flood control decision support system (IFCDSS) using statistical analysis to determine the relationship between the data, and integrate data mining technology for digital urban management based on Java EE. The system also provides location-based decision making in urban management by using the Baidu Maps API.

Guanlin Chen, Xinxin Sun, Shengquan Li, Jiang He, Jiawei Zhang

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 1, 161–177

Article | 30-November-2018

Research and Improvement of Apriori Algorithm Based on Hadoop

I. INTRODUCTION In the context of the development of big data “spraying wells”, there is frequently a close relationship between vast amounts of data[1]. Analysis and decision making through data mining have become the mainstream of social development. In order to better find the relevance of transaction data sets, some researchers have discovered the concept of association rule mining technology[2]. With the attention of many researchers at home and abroad caused by the conception of the

Gao Pengfei, Wang Jianguo, Liu Pengcheng

International Journal of Advanced Network, Monitoring and Controls, Volume 3 , ISSUE 3, 100–105

Article | 30-November-2018

Application of K-means Algorithm in Geological Disaster Monitoring System

the subset of data of interest. The data object segmentation variable determines the formation of clustering, which in turn affects the correct interpretation of the clustering results, and ultimately affects the stability of the clustering clusters after the new data objects are added. Before the K-means clustering related data mining, the sample data set related to the data mining clustering analysis should be extracted from the original data object set, and it is not necessary to use all the

Wang Jianguo, Xue Linyao

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

Research paper | 01-September-2014

KNOWLEDGE-BASED MODELING FOR PREDICTING CANE SUGAR CRYSTALLIZATION STATE

This paper proposes a knowledge-based model applied to an experimental scale evaporative cane sugar crystallization process, which combines the methods of offline and online knowledge acquisition. Firstly, a data mining method based on rough set theory is utilized to extract information from the large quantity of relevant data obtained in experiment. This method products an offline predictive knowledge. Thereafter, a method for online knowledge learning and self-improvement is put forward

Yanmei Meng, Xian Yu, Haiping He, Zhihong Tang, Xiaochun Wang, Jian Chen

International Journal on Smart Sensing and Intelligent Systems, Volume 7 , ISSUE 3, 942–965

Research Article | 01-September-2017

MULTI_LEVEL SECURE FROM WEB INTRUSION AND QUERY ATTACKS ON WEB DATABASE

Nirmala Kumari R, Mala V

International Journal on Smart Sensing and Intelligent Systems, Volume 10 , ISSUE 5, 271–283

Article | 06-November-2017

A REVIEW OF ARTIFICIAL FISH SWARM OPTIMIZATION METHODS AND APPLICATIONS

stochastic search. Fishes show very intelligently social behaviors. This algorithm is one of the best approaches of the Swarm Intelligence method with considerable advantages like high convergence speed, flexibility, error tolerance and high accuracy. this paper review the AFSA algorithm, its evolution stages from the start point up to now, improvements and applications in various fields like optimization, control, image processing, data mining, improving neural networks, networks, scheduling, and signal

Mehdi Neshat, Ali Adeli, Ghodrat Sepidnam, Mehdi Sargolzaei, Adel Najaran Toosi

International Journal on Smart Sensing and Intelligent Systems, Volume 5 , ISSUE 1, 108–148

research-article | 30-November-2019

A review of privacy-preserving human and human activity recognition

in full swing, but the task of privacy protection remains. Machine learning technology has been actively introduced in big data processing, and applied in many applications where mechanical data mining is difficult. However, privacy concerns are raised in applications that extract information through deep learning (Tanuwidjaja et al., 2019). Privacy protection is essential as the application of deep learning is expanded from medical applications that process sensitive information such as patient

Im Y. Jung

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

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