Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.Decision trees

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    Sound event detection in urban soundscape using two-level classification
    (Institute of Electrical and Electronics Engineers Inc., 2016) Luitel, B.; Vishnu Srinivasa Murthy, Y.V.S.; Koolagudi, S.G.
    A huge increase in automobile field h as lead t o the creation of different sounds in large volume, especially in urban cities. An analysis of the increased quantity of automobiles will give information related to traffic and vehicles. It also provides a scope to understand the scenario of particular location using sound scape information. In this paper, a two level classification is proposed to classify urban sound events such as bus engine (BE), bus horn (BH), car horn (CH) and whistle (W) sounds. The above sounds are taken as they place a major role in traffic scenario. A real-time data is collected from the live recordings at major locations of the urban city. Prior to the detection of events, the class of events is identified u sing signal processing techniques. Further, features such as Mel-frequency cepstral coefficients (MFCCs) a re extracted based on the analysis of a spectrum of the above-mentioned events and they are prominent to classify even in the complex scenario. Classifiers such as artificial neural networks (ANN), naive-Bayesian (NB), decision tree (J48), random forest (RF) are used at two levels. The proposed approach outperforms the existing approaches that usually does direct feature extraction without signal level analysis. © 2016 IEEE.
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    Predictive analytics and data mining in healthcare
    (Institute of Electrical and Electronics Engineers Inc., 2021) Arjun, A.; Srinath, A.; Chandavarkar, B.R.
    Machine Learning and Data Mining for healthcare. There has been an enormous growth in the field of HIT (health information technology) in the recent years. Be it detection of certain diseases, scanning of organs, finding tumors, these machine oriented operations without human intervention, have certainly increased the quality of medical attention one can get, and the technology required has come a long way. Health data tends to be inherently complex with exceptions in almost all cases. Data mining is the technique of converting raw data into a meaningful format. Analysis and prediction on such data, although computationally and algorithmically complex, is an emerging technology that is a small step to more proactive and preventive automated treatment options.There are various data mining techniques such as classification, clustering, association, regression,prediction, pattern recognition etc [I]. Even the efficiency of certain medicines can be found using machine learning techniques, which is a life saving and cost effective method. In this paper, we are going to use machine learning as a tool for predictive analysis to predict chronic kidney diseases based on the Chronic disease dataset taken from VCI M L repository. We will be applying machine learning algorithms, specifically decision trees, to build a classifier to predict if a person has the disease or not. This paper shows the issue that specific machine learning algorithms need to be tailor-made to specific nature of medical data. © 2021 IEEE.