An Efficacy of Spectral Features with Boosted Decision Tree Algorithm for Automatic Heart Sound Classification

This research work aims to classify the audio signals received from heart into normal/abnormal. The heart sound perceived has been referred as phonocardiogram (PCG) signals. An attempt has been made to identify a set of features that provide more accurate results for classifying PCG under designated categories using a variant of decision tree algorithm. After applying 6th order butter worth band-pass filter on PCG signals, the new features, viz. Tonnetz, Spectral contrast, and Chroma have been extracted. Further, XGBOOST, a variant of the decision tree has been used for classifying unsegmented PCG signals. The benchmark datasets, PhysioNet 2016, and PASCAL 2011 have been taken for validating the proposed methodology presented here. PhysioNet 2016 is comprised of sub-datasets, viz. A–F which contain a total of 3,240 PCG recordings, whereas the PASCAL 2011 contains 415 heart sound signals. The proposed approach considers a new feature set in conjunction with the existing ones; and it has resulted in mean accuracy, sensitivity, and specificity scores as 95.2, 94.22 and 96.18 respectively.

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THEFT DETECTION IN POWER UTILITIES USING ENSEMBLE OF CHAID DECISION TREE ALGORITHM

Science Proceedings Series ◽

10.31580/sps.v2i2.1480 ◽

2020 ◽

Vol 2 (2) ◽

pp. 161-165

Author(s):

Muhammad Salman Saeed ◽

Mohd. Wazir Mustafa ◽

Usman Ullah Sheikh ◽

Attaullah Khidrani ◽

Mohd Norzali Haji Mohd

Keyword(s):

Decision Tree ◽

Distribution System ◽

Research Work ◽

Machine Learning Algorithms ◽

Support Vector ◽

Decision Tree Algorithm ◽

Chi Square ◽

Tree Algorithm ◽

Interaction Detection ◽

Electricity Theft

Theft of electricity and fraud in energy consumption billing are the primary concerns for Distribution System Operators . Because of those illegal activities, it is believed that billions of dollars are wasted each year. DSOs around the world continue to use conventional time consuming and inefficient methods for non-technical loss detection, particularly in underdeveloped countries . This research work attempts to solve the problems as mentioned above by designing an effective model for detecting electricity theft to classify fraudster customers in a power delivery system. The key motivation for this current study is to support the DSOs in their fight against the stealing of electricity. Initially, the proposed method uses the monthly energy customer consumption data obtained from Multan Electric Power Company (MEPCO) Pakistan to segregate fraudsters and honest customers. The Bagged Chi - square Automatic Interaction Detection (CHAID) based decision tree algorithm is then used to classify the honest and fraudster consumers.Furthermore, based on the mentioned metrics, the performance superiority of the Bagged CHAID-based NTL detection method is validated by comparing its efficacy with that of few well-known state-of-the-art machine learning algorithms such as Artificial Neural Network (ANN), Support Vector Machine (SVM),) Logistic Regression (LR), Discriminant Analysis and Bayesian Network (BN).

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To Identify the Improvement Pattern of Self-financing Arts and Science College Student’s Academic Performance using Classification Algorithms

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1976.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 2429-2433

Keyword(s):

Academic Performance ◽

Decision Tree ◽

Naive Bayes ◽

Research Work ◽

Naïve Bayes ◽

Classification Algorithms ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Science College ◽

Bayes Algorithm

The aim of this research work is to identify the improvement pattern of academic performance of final year students of self-financing arts and science colleges. The data was collected from the students of nine Arts and Science Colleges. The data contains demographic, socio-economic, residence and college location, subjects, infrastructural facilities, faculty concern and self-motivation attributes. The classification algorithms like Naïve Bayes, Decision tree and CBPANN are applied on the student’s data. The outcome of the research can be used to improve the academic performance students studying in self-financing arts and science colleges located in educationally backward areas. The experiment results shows that the accuracy value for Naïve Bayes algorithm is 92.63%, accuracy value for Decision Tree algorithm is 96.41% and accuracy value for CBPANN algorithm is 99.49%

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