The past few years have witnessed increased interest in the potential use of wireless sensor networks (WSNs) in a wide range of applications and it has become a hot research area. Based on network structure, routing protocols in WSNs can be divided into two categories: flat routing and hierarchical or clustering routing. Owing to a variety of advantages, clustering is becoming an active branch of routing technology in WSNs. In this paper, we present a comprehensive and fine grained survey on clustering routing protocols proposed in the literature for WSNs. We outline the advantages and objectives of clustering for WSNs, and develop a novel taxonomy of WSN clustering routing methods based on complete and detailed clustering attributes. In particular, we systematically analyze a few prominent WSN clustering routing protocols and compare these different approaches according to our taxonomy and several significant metrics. Finally, we summarize and conclude the paper with some future directions.

https://www.mdpi.com/1424-8220/12/8/11113/pdf

A Survey on Clustering Routing Protocols in Wireless Sensor Networks

As new fabrication and integration technologies reduce the cost and size of micro-sensors and wireless interfaces, it becomes feasible to deploy densely distributed wireless networks of sensors and actuators. These systems promise to revolutionize biological, earth, and environmental monitoring applications, providing data at granularities unrealizable by other means. In addition to the challenges of miniaturization, new system architectures and new network algorithms must be developed to transform the vast quantity of raw sensor data into a manageable stream of high-level data. To address this, we propose a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.We briefly describe Habitat monitoring as our motivating application and introduce initial system building blocks designed to support this application. The remainder of the paper presents details of our experimental platform.

Habitat monitoring: Application driver for wireless communications technology

http://romisatriawahono.net/lecture/rm/survey/machine%20learning/Afsar%20-%20Clustering%20in%20Sensor%20Networks%20-%202014.pdf

Clustering in sensor networks

운동발달의 이해 = Understanding motor development

Egypt occupied the fifth place in both income and weight of tomato production.We proposed an automated multi-class classification approach for tomato ripeness stages.Performance of classification algorithms depends on statistics of the experimented dataset.Training and testing datasets have been generated via employing the 10-fold cross validation.Using OAO multi-class SVMs with linear kernel function outperformed other algorithms. Tomato quality is one of the most important factors that helps ensuring a consistent marketing of tomato fruit. As ripeness is the main indicator for tomato quality from customers perspective, the determination of tomato ripeness stages is a basic industrial concern regarding tomato production in order to get high quality product. Automatic ripeness evaluation of tomato is an essential research topic as it may prove benefits in ensuring optimum yield of high quality product, this will increase the income because tomato is one of the most important crops in the world. This article presents an automated multi-class classification approach for tomato ripeness measurement and evaluation via investigating and classifying the different maturity/ripeness stages. The proposed approach uses color features for classifying tomato ripeness stages. The approach proposed in this article uses Principal Components Analysis (PCA) in addition to Support Vector Machines (SVMs) and Linear Discriminant Analysis (LDA) algorithms for feature extraction and classification, respectively. Experiments have been conducted on a dataset of total 250 images that has been used for both training and testing datasets with 10-fold cross validation. Experimental results showed that the proposed classification approach has obtained ripeness classification accuracy of 90.80%, using one-against-one (OAO) multi-class SVMs algorithm with linear kernel function, ripeness classification accuracy of 84.80% using one-against-all (OAA) multi-class SVMs algorithm with linear kernel function, and ripeness classification accuracy of 84% using LDA algorithm.

Using machine learning techniques for evaluating tomato ripeness

There is several issues in Wireless Sensor Networks from which routing are also a major issue which is directly related to energy consumption. In order to increase the lifetime of network energy must be consumed efficiently. This paper describes various routing protocols and their features. We first describe types of routing protocols in wireless senor network and then explain the hierarchical routing protocols which include LEACH, PEGASIS, TEEN, APTEEN and CCPAR.

A Survey on Hierarchical Routing Protocols in Wireless Sensor Networks

A hybrid technique based on feature extraction and Principal Component Analysis (PCA) is presented for lung detection in CT scan images. Lung cancer, if detected successfully at early stages, enables many treatment options, reduced risk of invasive surgery and increased survival rate. .In this paper features are extracted using principal component analysis and Histogram Equalization is used for preprocessing of the images. The system produces promising results for lung cancer detection.

Feature Extraction and Principal Component Analysis for Lung Cancer Detection in CT scan Images

The early detection of lung cancer is a challenging problem, due to the structure of the cancer cells, where most of the cells are overlapped with each other. Classification is very important part of digital image analysis. It is a computational procedure that sort images into groups according to their similarities. In this paper Histogram Equalization is used for preprocessing of the images and feature extraction process and neural network classifier to check the state of a patient in its early stage whether it is normal or abnormal. After that we predict the survival rate of a patient by extracted features. Experimental analysis is made with dataset to evaluate the performance of the different classifiers. The performance is based on the correct and incorrect classification of the classifier. All experiments are conducted in WEKA data mining tool.

Early Detection and Prediction of Lung Cancer Survival using Neural Network Classifier

Lung Cancer is a disease of uncontrolled cell growth in tissues of the lung. Detection of Lung Cancer in its early stage is the key of its cure. In general, a measure for early stage lung cancer diagnosis mainly includes those utilizing X-ray chest films, CT, MRI etc. In many parts of the world widespread screening by CT or MRI is not yet practical, so that chest radiology remains in initial and most common procedure. Firstly, we will use some techniques are essential to the task of medical image mining, Lung Field Segmentation, Data Processing, Feature Extraction, Classification using neural network and SVMs. The methods used in this paper work states to classify digital X-ray chest films into two categories: normal and abnormal. Different learning experiments were performed on two different data sets, created by means of feature selection and SVMs trained with different parameters; the results are

A Study of Detection of Lung Cancer Using Data Mining Classification Techniques

2 Abstract: Web usage mining is a type of web mining which deals with log files for extracting the information about user browsing behavior. User future request prediction is an approach of web usage mining to predict the next web page for user. In this paper, KFCM method of fuzzy clustering is proposed to predict the user future requests. In this firstly log file data is collected and then preprocessed. After that clustering algorithms FCM and KFCM are implemented to predict the user future requests. The experimental results defining the betterment of KFCM for prediction.

Granth Sahib

Papers

A Survey on Hierarchical Routing Protocols in Wireless Sensor Networks

Feature Extraction and Principal Component Analysis for Lung Cancer Detection in CT scan Images

Early Detection and Prediction of Lung Cancer Survival using Neural Network Classifier

A Study of Detection of Lung Cancer Using Data Mining Classification Techniques

User Future Request Prediction Using KFCM in Web Usage Mining