Gas classification using binary decision tree classifier

Hassan, Muhammad; Bermak, Amine

doi:10.1109/iscas.2014.6865700

Cited by 15 publications

(6 citation statements)

References 8 publications

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“…Many forms of gas classification is made possible with the help of other data mining algorithms. As their results were not that much efficient in [5] they used binary decision tree algorithm for effective classification. The gas concentration is recorded using array of seven metal oxide gas sensor for five different gases.…”

Section: Related Workmentioning

confidence: 95%

Rule Generation for Gallbladder Cancer Prediction Using Decision Tree Classifier

Sharmila¹,

Santhosh²

2018

BIJDM

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Cancer is a disease, where cells grow out of control, divide and attack other tissues of the patient. There are many forms of cancer, among which the "Gallbladder Cancer" is relatively uncommon. This cancer can be permanently cured only by removing the Gallbladder. Else, the patient must be treated with high dosage of medicine for the survival, which in turn cures the disease but reduces the lifetime of the patient. Instead, if the gall bladder cancer is predicted at the earlier stage, it can be cured by treating with less dosage of medicines. In the proposed methodology, earlier detection of gall bladder cancer is done through imaging, which contains three major steps as, Pre-processing, Feature Extraction and Feature Classification. The noise in the image can be removed using Pre-processing step. The essential features for the cancer prediction can be extracted from the segmented image. Further, using decision tree algorithm, gallbladder cancer prediction is done.

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Section: Related Workmentioning

confidence: 95%

Rule Generation for Gallbladder Cancer Prediction Using Decision Tree Classifier

Sharmila¹,

Santhosh²

2018

BIJDM

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“…In order to detect the stellate lesions in digital mammograms, we perform the per-pixel classification algorithm using the well-known BDT [17]. Namely, for each pixel ( , ) in digital mammogram, we form a feature vector from the three statistical features ( ( , ) �� , ( , ) and ( , ) ) computed in Section 3.4.1.…”

Section: Classificationmentioning

confidence: 99%

Automatic Detection of Stellate Lesions in Digital Mammograms Using Multi-scale SIFT

Hikmat¹,

Afdel²,

Bakkouri³

2020

JPP

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This paper presents a novel automatic mammography recognition approach used to develop computer-aided diagnostic systems that require a robust method to assist the radiologist in identifying and recognizing speculations from a multitude of lines corresponding to the normal fibrous breast tissue. Following this rationale, this paper introduces a novel approach for detecting the speculated lesions in digital mammograms based on multi-scale SIFT (scale-invariant feature transform) orientations. The proposed method starts by estimating a set of key points that best represent the image mammography in a scale space. We then benefit from SIFT algorithm to locally characterize each key point by assigning a consistent orientation. Thereafter, a set of three features are extracted for each pixel in the image mammogram based on these orientations. The extracted features are fed into BDT (binary decision tree) in order to perform per pixel classification and decide whether the pixel is normal or abnormal. We evaluate the proposed system on BCDR (breast cancer digital repository) database and the experimental results show that our method is accurate with 97.95% recognition rate, while it is robust to illumination changes, rotation and scale variations.

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“…It has been claimed that both CKNN and tree-CKNN perform better that KNN by reaching an accuracy of 98.7% for CKNN and 100% respectively without applying dimensionality reduction techniques as preprocessing step. Another DT based EN is presented in [28]. Five gases are targeted in this work and seven sensors are used.…”

Section: Related Workmentioning

confidence: 99%

Embedded Platform for Gas Applications Using Hardware/Software Co-Design and RFID

et al. 2018

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This paper presents the development of a wireless low power reconfigurable self-calibrated multi-sensing platform for gas sensing applications. The proposed electronic nose (EN) system monitors gas temperatures, concentrations and mixtures wirelessly using the radio-frequency identification (RFID) technology. The EN takes the form of a set of gas and temperature sensors as well as multiple pattern recognition algorithms implemented on the Zynq system on chip (SoC) platform. The gas and temperature sensors are integrated on a semi-passive RFID tag to reduce the consumed power. Various gas sensors are tested including an in-house fabricated 4 × 4 SnO2 based sensor and 7 commercial Figaro sensors. The Data is transmitted to the Zynq based processing unit using a RFID reader where it is processed using multiple pattern recognition algorithms for dimensionality reduction and classification. Multiple algorithms are explored for optimum performance including principal component analysis (PCA) and linear discriminant analysis (LDA) for dimensionality reduction while decision tree (DT) and k-nearest neighbors (KNN) are assessed for classification purpose. Different gases are targeted at diverse concentration including carbon monoxide (CO), ethanol (C2H6O), carbon dioxide (CO2), propane (C3H8), ammonia (N H3) and hydrogen (H2). An accuracy of 100% is achieved in many cases with an overall accuracy above 90% in most scenarios. Finally, the hardware/software heterogeneous solution to implementation PCA, LDA, DT and KNN on the Zynq SoC shows promising results in terms of resources usage, power consumption and processing time.

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Gas classification using binary decision tree classifier

Cited by 15 publications

References 8 publications

Rule Generation for Gallbladder Cancer Prediction Using Decision Tree Classifier

Rule Generation for Gallbladder Cancer Prediction Using Decision Tree Classifier

Automatic Detection of Stellate Lesions in Digital Mammograms Using Multi-scale SIFT

Embedded Platform for Gas Applications Using Hardware/Software Co-Design and RFID

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