scholarly journals Automatic Classification Algorithm for Multisearch Data Association Rules in Wireless Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Cailing Li ◽  
Wenjun Li
Keyword(s):  
Wireless Network ◽  
Association Rules ◽  
Data Association ◽  
Automatic Classification ◽  
Classification Algorithm ◽  
Fuzzy Classification ◽  
Classification Error ◽  
Data Generation ◽  
High Coverage ◽  
Efficient Data

In order to realize efficient data processing in wireless network, this paper designs an automatic classification algorithm of multisearch data association rules in a wireless network. According to the algorithm, starting from the mining of multisearch data association rules, from the discretization of continuous attributes of multisearch data, generation of fuzzy classification rules, and the design of association rule classifier and other aspects, automatic classification is completed by using the mining results. Experimental results show that this algorithm has the advantages of small classification error, good real-time performance, high coverage rate, and high feasibility.

scholarly journals Automatic Classification Algorithm for Diffused Liver Diseases based on Ultrasound Images

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Ahmed Gaber ◽  
A. Hamdy ◽  
Hammam M. Abdelaal ◽  
Ahmed Elkattan ◽  
Motasem M. Elshourbagy ◽  
...  
Keyword(s):  
Liver Diseases ◽  
Automatic Classification ◽  
Classification Algorithm ◽  
Ultrasound Images

Efficient data association to targets for tracking in passive wireless sensor networks

2018 ◽  
Vol 75-76 ◽  
pp. 19-32 ◽  
Author(s):  
Changhyuk An ◽  
Youngwon Kim An ◽  
Seong-Moo Yoo ◽  
B. Earl Wells
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Data Association ◽  
Wireless Sensor ◽  
Efficient Data

scholarly journals An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Mengling Zhao ◽  
Hongwei Liu
Keyword(s):  
Association Rules ◽  
Large Scale ◽  
Classification Algorithm ◽  
Data Sets ◽  
Immune Network ◽  
Artificial Immune ◽  
Mining Algorithm ◽  
Sar Image Segmentation ◽  
Artificial Immune Network ◽  
Adaptive Pso

As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN). The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC) algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC), and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

Positional Correlative Anatomy of Invertebrate Model Organisms Increases Efficiency of TEM Data Production

2014 ◽  
Vol 20 (5) ◽  
pp. 1392-1403 ◽  
Author(s):  
Irina Kolotuev
Keyword(s):  
Developmental Biology ◽  
Cell Biology ◽  
Sampling Rate ◽  
Unmet Need ◽  
Region Of Interest ◽  
Model Organisms ◽  
Data Generation ◽  
Step Method ◽  
Efficient Data ◽  
Research Questions

AbstractTransmission electron microscopy (TEM) is an important tool for studies in cell biology, and is essential to address research questions from bacteria to animals. Recent technological innovations have advanced the entire field of TEM, yet classical techniques still prevail for most present-day studies. Indeed, the majority of cell and developmental biology studies that use TEM do not require cutting-edge methodologies, but rather fast and efficient data generation. Although access to state-of-the-art equipment is frequently problematic, standard TEM microscopes are typically available, even in modest research facilities. However, a major unmet need in standard TEM is the ability to quickly prepare and orient a sample to identify a region of interest. Here, I provide a detailed step-by-step method for a positional correlative anatomy approach to flat-embedded samples. These modifications make the TEM preparation and analytic procedures faster and more straightforward, supporting a higher sampling rate. To illustrate the modified procedures, I provide numerous examples addressing research questions in Caenorhabditis elegans and Drosophila. This method can be equally applied to address questions of cell and developmental biology in other small multicellular model organisms.

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