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Gigras, Yogita
- An Efficient Hierarchical Clustering Technique for Medical Diagnosis Using KNN Classifier
Authors
1 Department of Computer Science, The Northcap University, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 9, No 4 (2017), Pagination: 62-69Abstract
In this research article, an intelligent hierarchal clustering technique for medical diagnosis system has been proposed. Various hierarchical clustering techniques and their variants have been very much explored in the field of machine learning. However, these techniques are deterministic, needn't bother with a determined number of clusters and are stable. But, they are not scalable for high dimensional data set due to their non-linear correlations. In this paper, a new approach is proposed for medical data classification based on hierarchical clustering. The proposed technique has the following features (i) In each cycle, rather than ascertaining the centroids for new clusters, new centroids are assessed from centroids in past cycle; and (iii) In every run, rather than combining just a single match of items, various sets are converged in the meantime.Keywords
Clustering, Hierarchical Agglomerative Clustering, K-Nearest Neighbor (KNN), Feature Selection, Filter and Wrapper Model, Medical Data.- Mining Patterns for Clustering Using Modified K-Means and SVM (Support Vector Machine)
Authors
1 NorthCap University, Gurgaon, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 9, No 5 (2017), Pagination: 87-92Abstract
Data mining can be termed as a process of extracting patterns (knowledge) and posing query from data. Stored in database. Classification is one among of its concept and techniques. This research article is proposing a novel hybrid mining approach by using modified K-Means and Support vector machine algorithm. Modified K-Means utilized here for making the clusters from given dataset and SVM is utilized for classification (on clustered dataset obtained from modified K-means clustering). Experiments are performed over different datasets which are taken from UCI repository. Datasets which are used for comparing clustering algorithm are provided in Table 1 along with their details. Evaluations are done on different datasets of following parameters: Accuracy obtained from new algorithm and confusing matrix which is being created for every dataset. Additionally, proposed algorithms provide better result than other.
Keywords
Confusion Matrix, Clustering, K-Means, Modified K-Means, SVM (Support Vector Machine).References
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- A Survey on Air Quality Sensing and Management System Using IOT
Authors
1 Department of Computer Science and Engineering, The North Cap University, Gurugram, Haryana, IN
Source
Artificial Intelligent Systems and Machine Learning, Vol 10, No 5 (2018), Pagination: 115-119Abstract
The level of pollution is increasing day by day by lot of factors such as population, increase in number of vehicles, industrialization and urbanization which results in harmful effect on human wellbeing by directly affecting health of population exposed to the pollution. This paper had a solution of monitoring the air and noise pollution levels in industrial environment. Technology used in this paper is internet of things. The main objective of this paper is to introduce air pollution monitoring system using internet of things and this technology is capable of detecting pollutants on roads and measure various types of pollutants in air. This paper also reports the status of air pollution in particular city along with the temperature. This system will provide a low cost solution and provides good results in controlling the air pollution especially in urban areas.
To control this harmful level of pollution there is urgent need to design a system for sensing the level of pollution region wise and provide appropriate measures to be taken at that particular level of pollution.
Keywords
Internet of Things (IoT), CO Sensor, CO2 Sensor, Temperature and Humidity Sensor, Air Pollution, Arduino Microcontroller, Wi-Fi Module, LCD Display, Android, PM Levels.References
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