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Classifying Big Medical Data through Bootstrap Decision Forest Using Penalizing Attributes

Intelligent Automation & Soft Computing 2023年第6期36卷 3675-3690页

作者： v.Gowri v.vijaya chamundeeswari Department of CSE SRM Institute of Science and TechnologyRamapuram CampusChennaiTamilnaduIndia Department of CSE Saveetha Engineering CollegeChennaiTamilnaduIndia

Decision forest is a well-renowned machine learning technique to address the detection and prediction problems related to clinical ***,the tra-ditional decision forest(DF)algorithms have lower classification accuracy and cannot handle high-dimensional feature space *** this work,we pro-pose a bootstrap decision forest using penalizing attributes(BFPA)algorithm to predict heart disease with higher *** work integrates a significance-based attribute selection(SAS)algorithm with the BFPA classifier to improve the performance of the diagnostic system in identifying cardiac *** pro-posed SAS algorithm is used to determine the correlation among attributes and to select the optimum subset of feature space for learning and testing *** selects the optimal number of learning and testing data points as well as the density of trees in the forest to realize higher prediction accuracy in classifying imbalanced datasets *** effectiveness of the developed classifier is cautiously verified on the real-world database(i.e.,Heart disease dataset from UCI repository)by relating its enactment with many advanced approaches with respect to the accuracy,sensitivity,specificity,precision,and intersection over-union(IoU).The empirical results demonstrate that the intended classification approach outdoes other approaches with superior enactment regarding the accu-racy,precision,sensitivity,specificity,and IoU of 94.7%,99.2%,90.1%,91.1%,and 90.4%,***,we carry out Wilcoxon’s rank-sum test to determine whether our proposed classifier with feature selection method enables a noteworthy enhancement related to other classifiers or *** the experimental results,we can conclude that the integration of SAS and BFPA outperforms other classifiers recently reported in the literature.

关键词： Data classification decision forest feature selection healthcare data heart disease prediction penalizing attributes

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An Approach towards Pulse Data Transmission Using Modified Negative Luo Converter (MNLC) for Telecoms

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Circuits and Systems 2016年第9期7卷 2712-2728页

作者： v. chamundeeswari Dr. R. Seyezhai Department of EEE St. Joseph’s College of Engineering Chennai India Department of EEE SSN College of Engineering Chennai India

Telecom sectors generally operate at negative voltages to reduce the effect of corrosion caused in the metallic wire due to electrochemical reaction while communicating signals. To feed those lines and to have an effective digital data transmission, a power electronic converter referred as Modified Negative Luo Converter (MNLC) is proposed in this paper. MNLC is a high gain converter in which the output voltage increases in geometric progression. This paper portrays a novel concept of a 50 Hz pulse data transmission through RLCG (Resistance-inductance-capacitance with a shunt conductance) transmission line using MNLC. Signal frequency of 50 Hz to be transmitted is anded with a high frequency pulse that charges and discharges MNLC and produces the boosted negative output voltage. The boosted output is again transmitted through the RLCG transmission line from which 50 Hz data pulse is retrieved at the output of the transmission line by comparing with a comparator signal. This sort of MNLC aided data transmission not only introduces less loss in its transmitted data but also overcomes various health hazards of conventional radio frequency (RF) communication. This technique also proves that any data bit stream can be transmitted and retrieved using the proposed high gain DC-DC converter. The simulation model of the proposed system is implemented in MATLAB for various switching frequencies with its prototype of the converter developed and the results are verified.

关键词： Pulse Data Transmission MNLC RLCG Line Switching Frequency High Gain Converter

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