This document presents research on classifying cardiac arrhythmias using frequency domain features extracted from electrocardiogram (ECG) signals. Features are extracted from ECG data using Discrete Cosine Transform to calculate the distance between RR waves. These frequency domain features are then classified using various machine learning algorithms, including Classification and Regression Trees, Radial Basis Function networks, Support Vector Machines, and Multilayer Perceptron Neural Networks. Experiments were conducted on the MIT-BIH arrhythmia database to evaluate the performance of the different classifiers.
Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic ...CSCJournals
The electrocardiograph (ECG) contains cardiac features unique to each individual. By analyzing ECG, it should therefore be possible not only to detect the rate and consistency of heartbeats but to also extract other signal features in order to identify ECG records belonging to individual subjects. In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Eighteen temporal, amplitude, width and autoregressive (AR) model parameters are extracted from each ECG beat and classified in order to identify each individual. Proposed system uses pre-processing stage to decrease the effects of noise and other unwanted artifacts usually present in raw ECG data. Following pre-processing steps, ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. Window estimation is based on the localization of the R peaks in the ECG stream that detected by Filter bank method for QRS complex detection. ECG features – temporal, amplitude and AR coefficients are then extracted and used as an input to K-nn and SVM classification algorithms in order to identify the individual subjects and beats. Signal pre-processing techniques, applied feature extraction methods and some intermediate and final classification results are presented in this paper.
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...csitconf
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
Supervised Feature Selection for Diagnosis of Coronary Artery Disease Based o...cscpconf
Feature Selection (FS) has become the focus of much research on decision support systems areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapped Bayes Naïve (BN) based FS. Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final set of attribute contains the most relevant feature model that increases the accuracy. The algorithm in this case produces 85.50% classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then compared with the use of Support Vector Machine (SVM), Multi-Layer erceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is correspondingly compared with other FS algorithms. The Obtained results have shown very promising outcomes for the diagnosis of CAD.
Diagnosis of rheumatoid arthritis using an ensemble learning approachcsandit
Rheumatoid arthritis is one of the diseases that it
s cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnos
is and treatment of the disease. In this
study, a predictive model is suggested that diagnos
es rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients
referred to rheumatology clinic. For each
patient a record consists of several clinical and d
emographic features is saved. After data
analysis and pre-processing operations, three diffe
rent methods are combined to choose proper
features among all the features. Various data class
ification algorithms were applied on these
features. Among these algorithms Adaboost had the h
ighest precision. In this paper, we
proposed a new classification algorithm entitled CS
-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboos
t algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of
Adaboost in predicting of Rheumatoid
Arthritis.
Analysis of Human Electrocardiogram for Biometric Recognition Using Analytic ...CSCJournals
The electrocardiograph (ECG) contains cardiac features unique to each individual. By analyzing ECG, it should therefore be possible not only to detect the rate and consistency of heartbeats but to also extract other signal features in order to identify ECG records belonging to individual subjects. In this paper, a new approach for automatic analysis of single lead ECG for human recognition is proposed and evaluated. Eighteen temporal, amplitude, width and autoregressive (AR) model parameters are extracted from each ECG beat and classified in order to identify each individual. Proposed system uses pre-processing stage to decrease the effects of noise and other unwanted artifacts usually present in raw ECG data. Following pre-processing steps, ECG stream is partitioned into separate windows where each window includes single beat of ECG signal. Window estimation is based on the localization of the R peaks in the ECG stream that detected by Filter bank method for QRS complex detection. ECG features – temporal, amplitude and AR coefficients are then extracted and used as an input to K-nn and SVM classification algorithms in order to identify the individual subjects and beats. Signal pre-processing techniques, applied feature extraction methods and some intermediate and final classification results are presented in this paper.
SUPERVISED FEATURE SELECTION FOR DIAGNOSIS OF CORONARY ARTERY DISEASE BASED O...csitconf
Feature Selection (FS) has become the focus of much research on decision support systems
areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic
Algorithm (GA) wrapped Bayes Naïve (BN) based FS.
Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the
second step of the selection procedure. The final set of attribute contains the most relevant
feature model that increases the accuracy. The algorithm in this case produces 85.50%
classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then
compared with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and
C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are
respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is
correspondingly compared with other FS algorithms. The Obtained results have shown very
promising outcomes for the diagnosis of CAD.
Supervised Feature Selection for Diagnosis of Coronary Artery Disease Based o...cscpconf
Feature Selection (FS) has become the focus of much research on decision support systems areas for which datasets with tremendous number of variables are analyzed. In this paper we
present a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapped Bayes Naïve (BN) based FS. Basically, CAD dataset contains two classes defined with 13 features. In GA–BN algorithm, GA
generates in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final set of attribute contains the most relevant feature model that increases the accuracy. The algorithm in this case produces 85.50% classification accuracy in the diagnosis of CAD. Thus, the asset of the Algorithm is then compared with the use of Support Vector Machine (SVM), Multi-Layer erceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Consequently, the GA wrapped BN Algorithm is correspondingly compared with other FS algorithms. The Obtained results have shown very promising outcomes for the diagnosis of CAD.
Diagnosis of rheumatoid arthritis using an ensemble learning approachcsandit
Rheumatoid arthritis is one of the diseases that it
s cause is unknown yet; exploring the field of
medical data mining can be helpful in early diagnos
is and treatment of the disease. In this
study, a predictive model is suggested that diagnos
es rheumatoid arthritis. The rheumatoid
arthritis dataset was collected from 2,564 patients
referred to rheumatology clinic. For each
patient a record consists of several clinical and d
emographic features is saved. After data
analysis and pre-processing operations, three diffe
rent methods are combined to choose proper
features among all the features. Various data class
ification algorithms were applied on these
features. Among these algorithms Adaboost had the h
ighest precision. In this paper, we
proposed a new classification algorithm entitled CS
-Boost that employs Cuckoo search
algorithm for optimizing the performance of Adaboos
t algorithm. Experimental results show
that the CS-Boost algorithm enhance the accuracy of
Adaboost in predicting of Rheumatoid
Arthritis.
St variability assessment based on complexity factor using independent compon...eSAT Journals
Abstract
In recent days the computerized ECG has become the most effective and convenient diagnostic tool to identify cardiac diseases
such as Myocardial Ischemia (MI). Among the Cardio vascular diseases (CVDs) the Myocardial Ischemia (MI) is one of the
leading causes of heart attacks. The Myocardial Ischemia (MI) occurs due to the difficulties in the flow of the electrical impulses
from SA node to bundle branches because of the abnormalities in the conduction system. Normally the ECG is used as a main
diagnostic tool to identify the cardiac diseases. In order to obtain accurate information from ECG it is necessary to remove all the
artifacts and extract the pure ECG from noise background. In this paper the removal of the artifacts is achieved with linear
filtering and the extraction of the clean ECG signal is performed using Independent Component Analysis (ICA). After
preprocessing and ECG extraction, the QRS complex of each beat is detected by using Hilbert Transform and simple threshold
detection algorithm. Next the Instantaneous Heart Rate (IHR) from RR interval and Complexity Factor (CF) from time series ST
segment are computed for each beat to form desired feature sets. Later a linear regression model is designed using Instantaneous
heart rate (IHR) and ST segment Complexity Factors (STCFs) based on Linear Regression analysis. The proposed ICA-STCFR
model is used to identify the ischemic beats from the test feature sets of ECG signal to assess the ST-Segment Variability (STV).
The ECG data sets obtained from a local hospital were used to design and test the model. The evaluation parameters, Ischemic
Intensity Factor (IIF), Ischemic Activity Factors (IAF) and Peak to Average Value (PAV) were used to evaluate the proposed
method and compared with Wavelet Transform based method. The proposed ICA-STCFR was found to be yielding better results
than WT-ST method.
Key Words: Myocardial Ischemia, ICA, HT, QRS Complex, RR interval, ST segments, IHR, STCF, Scatter-plot
Real-Time Detection of Fatal Ventricular Dysrhythmias for Automated External ...Ehsan Izadi
Automated external defibrillators (AEDs) are
portable devices assigned to appropriate and real-time diagnosis
of two fatal dysrhythmias including, ventricular fibrillation and
rapid ventricular tachycardia; these are often associated with
sudden cardiac arrest. In this paper, a novel time-domain based
algorithm has been proposed for AEDs. The algorithm could
measure the heart rate and duration of the QRS Complex using
the critical points of electrocardiogram (ECG) to classify the
arrhythmias. This algorithm was tested with a large amount of
annotated data under equal conditions. The complete MIT-BIH
arrhythmia database, MIT-BIH normal sinus rhythm database,
MIT-BIH malignant database, and CU database were used as
the test data. The results obtained by the proposed algorithm
showed the sensitivity of 95.87%, the specificity of 99.00%, and
the accuracy of 98.78% in the single diagnose mode (SDM),
where the final decision was based on the last diagnose. On
the triple diagnose mode (TDM), where the final decision was
based on the last three consecutive diagnoses, the sensitivity of
94.50%, the specificity of 99.33%, and the accuracy of 99.07%
were obtained. In addition, the algorithm ensured the safety
of normal sinus rhythm cases with the specificity of 99.967%
and 99.997% in SDM and TDM, respectively. Furthermore, the
performance of the algorithm was calculated and plotted point
by point for different values. The proposed work was, therefore,
successfully implemented on ARM Cortex-M3 and evaluated
using test databases. Thus, this work could be well-suited for realtime
implementation in the AEDs and ECG monitoring devices
Classification of cardiac vascular disease from ecg signals for enhancing mod...hiij
“Why to be in frustration we will do new creation f
or salvation”. Based on these words we grapes your
attention towards saving a life of a heart patient
with the use of ECG in Public Health Care Center by
transmitting ECG signals to nearby hospital server.
In this paper we analyze the abnormalities found i
n the
ECG signals by identifying the Normal, Bradycardia
Arrhythmia, Tachycardia Arrhythmia and Ischemia
signal using the method of Neuro Fuzzy Classifier.
Daubechies Wavelet Transforms is used for feature
extraction and Adaptive Neuro Fuzzy Inference Syste
m (ANFIS) is used for classification. The compressi
on
algorithm is performed by using Huffman coding.
PERFORMANCE ANALYSIS OF MULTICLASS SUPPORT VECTOR MACHINE CLASSIFICATION FOR ...ijcsa
Automatic diagnosis of coronary heart disease helps the doctor to support in decision making a diagnosis.
Coronary heart disease have some types or levels. Referring to the UCI Repository dataset, it divided into 4
types or levels that are labeled numbers 1-4 (low, medium, high and serious). The diagnosis models can be
analyzed with multi class classification approach. One of multi class classification approach used, one of
which is a support vector machine (SVM). The SVM use due to strong performance of SVM in binary classification. This research study multiclass performance classification support vector machine to diagnose the type or level of coronary heart disease. Coronary heart disease patient data taken from the
UCI Repository. Stages in this study is preprocessing, which consist of, to normalizing the data, divide the data into data training and testing. The next stage of multiclass classification and performance analysis.This study uses multiclass SVM algorithm, namely: Binary Tree Support Vector Machine (BTSVM), OneAgainst-One (OAO), One-Against-All (OAA), Decision Direct Acyclic Graph (DDAG) and Exhaustive
Output Error Correction Code ( ECOC). Performance parameter used is recall, precision, F-measure and
Overall accuracy. The experiment results showed that the multiclass SVM classification algorithm with the
algorithm BT-SVM, OAA-SVM and the ECOC-SVM,gave the highest Recall in the diagnosis of type or
healthy level with a value above 90%, precision 82.143% and 86.793% F-measure,. For all kinds of
algorithms, except binary OAA-SVM algorithm gave the highest recall 0.0% for the type or level of sickhigh
and sick-serious, and ECOC- SVM algorithm gave the highest recall 0.0% for sick-medium and sickserious.
While the type or level other, the performance of recall, precision and F-measure between 20% -
30%,. The conclusion that can be drawn is that the approach to the multiclass classification algorithm BTSVM,
OAO-SVM, DDAG-SVM to diagnosis the type or level of coronary heart disease provides better performance, than the binary classification approach.
Classification of Cardiac Arrhythmia using WT, HRV, and Fuzzy C-Means ClusteringCSCJournals
The classification of the electrocardiogram registration into different pathologies disease devises is a complex pattern recognition task. In this paper, we propose a generic feature extraction for classification of ECG arrhythmias using a fuzzy c-means (FCM) clustering and Heart Rate variability (HRV). The traditional methods of diagnosis and classification present some inconveniences; seen that the precision of credit note one diagnosis exact depends on the cardiologist experience and the rate concentration. Due to the high mortality rate of heart diseases, early detection and precise discrimination of ECG arrhythmia is essential for the treatment of patients. During the recording of ECG signal, different forms of noise can be superimposed in the useful signal. The pre-treatment of ECG imposes the suppression of these perturbation signals. The row date is preprocessed, normalized and then data points are clustered using FCM technique. In this work, four different structures, FCM-HRV, PCM-HRV, FCMC-HRV and FPCM-HRV are formed by using heart rate variability technique and fuzzy c-means clustering. In addition, FCM-HRV is the new method proposed for classification of ECG. This paper presents a comparative study of the classification accuracy of ECG signals by using these four structures for computationally efficient diagnosis. The ECG signals taken from MIT-BIH ECG database are used in training to classify 4 different arrhythmias (Atrial Fibrillation Termination). All of the structures are tested by using the same ECG records. The test results suggest that FCMC-HRV structure can generalize better and is faster than the other structures.
A 52-year-old man with a medical history of hypertension presented to the emergency department with persistent chest pain of 6hours’ duration. Electrocardiography performed on arrival revealed anterior ST elevation. He was referred to the cardiac catheterization laboratory for Primary Percutaneous Coronary Intervention (PPCI). The coronary angiogram revealed total proximal Left Anterior Descending Artery (LAD) occlusion. The LAD stenosis was successfully treated with a drug-eluting stent. He experienced extreme thoraco abdominal pain within six hours after PPCI. Thoraco abdominal aortic Computed Tomography Angiography (CTA) was performed to rule out an aortic dissection. Eventually, the patient was successfully treated with endovascular graft exclusion.
AR-based Method for ECG Classification and Patient RecognitionCSCJournals
The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
My co-authors and I have created an R package that allows the user to perform a fully quantitative analysis of DCE-MRI (dynamic contrast-enhanced magnetic resonance imaging) data. With applications in oncology in mind, users can interrogate the perfusion characteristics of tissue in order to compare between treatment groups and pre-/post-treatment.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Comparative dosimetry of forward and inverse treatment planning for Intensity...iosrjce
IOSR Journal of Applied Physics (IOSR-JAP) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
St variability assessment based on complexity factor using independent compon...eSAT Journals
Abstract
In recent days the computerized ECG has become the most effective and convenient diagnostic tool to identify cardiac diseases
such as Myocardial Ischemia (MI). Among the Cardio vascular diseases (CVDs) the Myocardial Ischemia (MI) is one of the
leading causes of heart attacks. The Myocardial Ischemia (MI) occurs due to the difficulties in the flow of the electrical impulses
from SA node to bundle branches because of the abnormalities in the conduction system. Normally the ECG is used as a main
diagnostic tool to identify the cardiac diseases. In order to obtain accurate information from ECG it is necessary to remove all the
artifacts and extract the pure ECG from noise background. In this paper the removal of the artifacts is achieved with linear
filtering and the extraction of the clean ECG signal is performed using Independent Component Analysis (ICA). After
preprocessing and ECG extraction, the QRS complex of each beat is detected by using Hilbert Transform and simple threshold
detection algorithm. Next the Instantaneous Heart Rate (IHR) from RR interval and Complexity Factor (CF) from time series ST
segment are computed for each beat to form desired feature sets. Later a linear regression model is designed using Instantaneous
heart rate (IHR) and ST segment Complexity Factors (STCFs) based on Linear Regression analysis. The proposed ICA-STCFR
model is used to identify the ischemic beats from the test feature sets of ECG signal to assess the ST-Segment Variability (STV).
The ECG data sets obtained from a local hospital were used to design and test the model. The evaluation parameters, Ischemic
Intensity Factor (IIF), Ischemic Activity Factors (IAF) and Peak to Average Value (PAV) were used to evaluate the proposed
method and compared with Wavelet Transform based method. The proposed ICA-STCFR was found to be yielding better results
than WT-ST method.
Key Words: Myocardial Ischemia, ICA, HT, QRS Complex, RR interval, ST segments, IHR, STCF, Scatter-plot
Real-Time Detection of Fatal Ventricular Dysrhythmias for Automated External ...Ehsan Izadi
Automated external defibrillators (AEDs) are
portable devices assigned to appropriate and real-time diagnosis
of two fatal dysrhythmias including, ventricular fibrillation and
rapid ventricular tachycardia; these are often associated with
sudden cardiac arrest. In this paper, a novel time-domain based
algorithm has been proposed for AEDs. The algorithm could
measure the heart rate and duration of the QRS Complex using
the critical points of electrocardiogram (ECG) to classify the
arrhythmias. This algorithm was tested with a large amount of
annotated data under equal conditions. The complete MIT-BIH
arrhythmia database, MIT-BIH normal sinus rhythm database,
MIT-BIH malignant database, and CU database were used as
the test data. The results obtained by the proposed algorithm
showed the sensitivity of 95.87%, the specificity of 99.00%, and
the accuracy of 98.78% in the single diagnose mode (SDM),
where the final decision was based on the last diagnose. On
the triple diagnose mode (TDM), where the final decision was
based on the last three consecutive diagnoses, the sensitivity of
94.50%, the specificity of 99.33%, and the accuracy of 99.07%
were obtained. In addition, the algorithm ensured the safety
of normal sinus rhythm cases with the specificity of 99.967%
and 99.997% in SDM and TDM, respectively. Furthermore, the
performance of the algorithm was calculated and plotted point
by point for different values. The proposed work was, therefore,
successfully implemented on ARM Cortex-M3 and evaluated
using test databases. Thus, this work could be well-suited for realtime
implementation in the AEDs and ECG monitoring devices
Classification of cardiac vascular disease from ecg signals for enhancing mod...hiij
“Why to be in frustration we will do new creation f
or salvation”. Based on these words we grapes your
attention towards saving a life of a heart patient
with the use of ECG in Public Health Care Center by
transmitting ECG signals to nearby hospital server.
In this paper we analyze the abnormalities found i
n the
ECG signals by identifying the Normal, Bradycardia
Arrhythmia, Tachycardia Arrhythmia and Ischemia
signal using the method of Neuro Fuzzy Classifier.
Daubechies Wavelet Transforms is used for feature
extraction and Adaptive Neuro Fuzzy Inference Syste
m (ANFIS) is used for classification. The compressi
on
algorithm is performed by using Huffman coding.
PERFORMANCE ANALYSIS OF MULTICLASS SUPPORT VECTOR MACHINE CLASSIFICATION FOR ...ijcsa
Automatic diagnosis of coronary heart disease helps the doctor to support in decision making a diagnosis.
Coronary heart disease have some types or levels. Referring to the UCI Repository dataset, it divided into 4
types or levels that are labeled numbers 1-4 (low, medium, high and serious). The diagnosis models can be
analyzed with multi class classification approach. One of multi class classification approach used, one of
which is a support vector machine (SVM). The SVM use due to strong performance of SVM in binary classification. This research study multiclass performance classification support vector machine to diagnose the type or level of coronary heart disease. Coronary heart disease patient data taken from the
UCI Repository. Stages in this study is preprocessing, which consist of, to normalizing the data, divide the data into data training and testing. The next stage of multiclass classification and performance analysis.This study uses multiclass SVM algorithm, namely: Binary Tree Support Vector Machine (BTSVM), OneAgainst-One (OAO), One-Against-All (OAA), Decision Direct Acyclic Graph (DDAG) and Exhaustive
Output Error Correction Code ( ECOC). Performance parameter used is recall, precision, F-measure and
Overall accuracy. The experiment results showed that the multiclass SVM classification algorithm with the
algorithm BT-SVM, OAA-SVM and the ECOC-SVM,gave the highest Recall in the diagnosis of type or
healthy level with a value above 90%, precision 82.143% and 86.793% F-measure,. For all kinds of
algorithms, except binary OAA-SVM algorithm gave the highest recall 0.0% for the type or level of sickhigh
and sick-serious, and ECOC- SVM algorithm gave the highest recall 0.0% for sick-medium and sickserious.
While the type or level other, the performance of recall, precision and F-measure between 20% -
30%,. The conclusion that can be drawn is that the approach to the multiclass classification algorithm BTSVM,
OAO-SVM, DDAG-SVM to diagnosis the type or level of coronary heart disease provides better performance, than the binary classification approach.
Classification of Cardiac Arrhythmia using WT, HRV, and Fuzzy C-Means ClusteringCSCJournals
The classification of the electrocardiogram registration into different pathologies disease devises is a complex pattern recognition task. In this paper, we propose a generic feature extraction for classification of ECG arrhythmias using a fuzzy c-means (FCM) clustering and Heart Rate variability (HRV). The traditional methods of diagnosis and classification present some inconveniences; seen that the precision of credit note one diagnosis exact depends on the cardiologist experience and the rate concentration. Due to the high mortality rate of heart diseases, early detection and precise discrimination of ECG arrhythmia is essential for the treatment of patients. During the recording of ECG signal, different forms of noise can be superimposed in the useful signal. The pre-treatment of ECG imposes the suppression of these perturbation signals. The row date is preprocessed, normalized and then data points are clustered using FCM technique. In this work, four different structures, FCM-HRV, PCM-HRV, FCMC-HRV and FPCM-HRV are formed by using heart rate variability technique and fuzzy c-means clustering. In addition, FCM-HRV is the new method proposed for classification of ECG. This paper presents a comparative study of the classification accuracy of ECG signals by using these four structures for computationally efficient diagnosis. The ECG signals taken from MIT-BIH ECG database are used in training to classify 4 different arrhythmias (Atrial Fibrillation Termination). All of the structures are tested by using the same ECG records. The test results suggest that FCMC-HRV structure can generalize better and is faster than the other structures.
A 52-year-old man with a medical history of hypertension presented to the emergency department with persistent chest pain of 6hours’ duration. Electrocardiography performed on arrival revealed anterior ST elevation. He was referred to the cardiac catheterization laboratory for Primary Percutaneous Coronary Intervention (PPCI). The coronary angiogram revealed total proximal Left Anterior Descending Artery (LAD) occlusion. The LAD stenosis was successfully treated with a drug-eluting stent. He experienced extreme thoraco abdominal pain within six hours after PPCI. Thoraco abdominal aortic Computed Tomography Angiography (CTA) was performed to rule out an aortic dissection. Eventually, the patient was successfully treated with endovascular graft exclusion.
AR-based Method for ECG Classification and Patient RecognitionCSCJournals
The electrocardiogram (ECG) is the recording of heart activity obtained by measuring the signals from electrical contacts placed on the skin of the patient. By analyzing ECG, it is possible to detect the rate and consistency of heartbeats and identify possible irregularities in heart operation. This paper describes a set of techniques employed to pre-process the ECG signals and extract a set of features – autoregressive (AR) signal parameters used to characterise ECG signal. Extracted parameters are in this work used to accomplish two tasks. Firstly, AR features belonging to each ECG signal are classified in groups corresponding to three different heart conditions – normal, arrhythmia and ventricular arrhythmia. Obtained classification results indicate accurate, zero-error classification of patients according to their heart condition using the proposed method. Sets of extracted AR coefficients are then extended by adding an additional parameter – power of AR modelling error and a suitability of developed technique for individual patient identification is investigated. Individual feature sets for each group of detected QRS sections are classified in p clusters where p represents the number of patients in each group. Developed system has been tested using ECG signals available in MIT/BIH and Politecnico of Milano VCG/ECG database. Achieved recognition rates indicate that patient identification using ECG signals could be considered as a possible approach in some applications using the system developed in this work. Pre-processing stages, applied parameter extraction techniques and some intermediate and final classification results are described and presented in this paper.
My co-authors and I have created an R package that allows the user to perform a fully quantitative analysis of DCE-MRI (dynamic contrast-enhanced magnetic resonance imaging) data. With applications in oncology in mind, users can interrogate the perfusion characteristics of tissue in order to compare between treatment groups and pre-/post-treatment.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Comparative dosimetry of forward and inverse treatment planning for Intensity...iosrjce
IOSR Journal of Applied Physics (IOSR-JAP) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of physics and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in applied physics. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
WAVELET SCATTERING TRANSFORM FOR ECG CARDIOVASCULAR DISEASE CLASSIFICATIONgerogepatton
Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this
field is increasingly on prediction, with a growing dependence on machine learning techniques. This study
aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet
database by employing machine learning (ML). The study proposed several multi-class classification
models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart
rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG
signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral
features, and wavelet scattering, to extract features and capture unique characteristics from the ECG
dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work
provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease.
Furthermore, it could prove to be a valuable resource for future medical research projects aimed at
improving the diagnosis and treatment of cardiovascular diseases
WAVELET SCATTERING TRANSFORM FOR ECG CARDIOVASCULAR DISEASE CLASSIFICATIONijaia
Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this
field is increasingly on prediction, with a growing dependence on machine learning techniques. This study
aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet
database by employing machine learning (ML). The study proposed several multi-class classification
models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart
rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG
signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral
features, and wavelet scattering, to extract features and capture unique characteristics from the ECG
dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work
provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease.
Furthermore, it could prove to be a valuable resource for future medical research projects aimed at
improving the diagnosis and treatment of cardiovascular diseases.
WAVELET SCATTERING TRANSFORM FOR ECG CARDIOVASCULAR DISEASE CLASSIFICATIONgerogepatton
Classifying the ECG dataset is the main technique for diagnosing heart disease. However, the focus of this
field is increasingly on prediction, with a growing dependence on machine learning techniques. This study
aimed to enhance the accuracy of cardiovascular disease classification using data from the PhysioNet
database by employing machine learning (ML). The study proposed several multi-class classification
models that accurately identify patterns within three classes: heart failure rhythm (HFR), normal heart
rhythm (NHR), and arrhythmia (ARR). This was accomplished by utilizing a database containing 162 ECG
signals. The study employed a variety of techniques, including frequency-time domain analysis, spectral
features, and wavelet scattering, to extract features and capture unique characteristics from the ECG
dataset. The SVM model produced a training accuracy of 97.1% and a testing accuracy of 92%. This work
provides a reliable, effective, and human error-free diagnostic tool for identifying heart disease.
Furthermore, it could prove to be a valuable resource for future medical research projects aimed at
improving the diagnosis and treatment of cardiovascular diseases.
An Automated ECG Signal Diagnosing Methodology using Random Forest Classifica...ijtsrd
In this project, we put forward a new automated quality aware ECG beat classification method for effectual diagnosis of ECG arrhythmias under unsubstantiated health concern environments. The suggested method contains three foremost junctures i ECG signal quality assessment ECG SQA based whether it is “acceptable†or “unacceptable†based on our preceding adapted complete ensemble empirical mode decomposition CEEMD and temporal features, ii reconstruction of ECG signal and R peak detection iii the ECG beat classification as well as the ECG beat extraction, beat alignment and Random forest RF based beat classification. The accuracy and robustness of the anticipated method is evaluated by means of different normal and abnormal ECG signals taken from the standard MIT BIH arrhythmia database. The suggested ECG beat extraction approach can recover the categorization accuracy by protecting the QRS complex portion and background noises is suppressed under an acceptable level of noise . The quality aware ECG beat classification techniques attains higher kappa values for the classification accuracies which can be reliable as evaluated to the heartbeat classification methods without the ECG quality assessment process. Akshara Jayanthan M B | Prof. K. Kalai Selvi "An Automated ECG Signal Diagnosing Methodology using Random Forest Classification with Quality Aware Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30750.pdf Paper Url :https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/30750/an-automated-ecg-signal-diagnosing-methodology-using-random-forest-classification-with-quality-aware-techniques/akshara-jayanthan-m-b
Photoplethysmography (PPG) and Phonocardiography (PCG) are two important non-invasive techniques for monitoring physiological parameters of cardiovascular diagnostics. The PCG signal discloses information about cardiac function through vibrations caused by the working heart. PPG measures relative blood volume changes in the blood vessels close to the skin. This paper emphasizes on simultaneous acquisition of PCG and PPG signals from the same subject with the aid of NIELVIS II+ DAQ and the signals are imported to MATLAB for further processing. Heart rate is extracted from both the signals which are found to be distinctive. This analytical approach of processing these signals can abet for analysis of Heart rate variability (HRV) which is widely used for quantifying neural cardiac control and low variability is particularly predictive of death in patients after myocardial infarction.
Accuracy Assessment for Multi-Channel ECG Waveforms Using Soft Computing Meth...IJERA Editor
ECG waveform rhythmic analysis is very important. In recent trends, analysis processes of ECG waveform applications are available in smart devices. Still, existing methods are not able to accomplish the complete accuracy assessment while classify the multi-channel ECG waveforms. In this paper, proposed analysis of accuracy assessment of the classification of multi-channel ECG waveforms using most popular Soft Computing algorithms. In this research, main focus is on the better rule generation to analyze the multi-channel ECG waveforms. Analysis is mainly done inSoft Computing methods like the Decision Trees with different pruning analysis, Logistic Model Trees with different regression process and Support Vector Machine with Particle Swarm Optimization (SVM-PSO). All these analysis methods are trained and tested with MIT-BIH 12 channel ECG waveforms. Before trained these methods, MSO-FIR filter should be used as data preprocessing for removal of noise from original multi-channel ECG waveforms. MSO technique is used for automatically finding out the cutoff frequency of multichannel ECG waveforms which is used in low-pass filtering process. The classification performance is discussed using mean squared error, member function, classification accuracy, complexity of design, and area under curve on MIT-BIH data. Additionally, this research work is extended for the samples of multi-channel ECG waveforms from the Scope diagnostic center, Hyderabad. Our study assets the best process using the Soft Computing methods for analysis of multi-channel ECG waveforms
1-dimensional convolutional neural networks for predicting sudden cardiacIAESIJAI
Sudden cardiac arrest (SCA) is a serious heart problem that occurs without symptoms or warning. SCA causes high mortality. Therefore, it is important to estimate the incidence of SCA. Current methods for predicting ventricular fibrillation (VF) episodes require monitoring patients over time, resulting in no complications. New technologies, especially machine learning, are gaining popularity due to the benefits they provide. However, most existing systems rely on manual processes, which can lead to inefficiencies in disseminating patient information. On the other hand, existing deep learning methods rely on large data sets that are not publicly available. In this study, we propose a deep learning method based on one-dimensional convolutional neural networks to learn to use discrete fourier transform (DFT) features in raw electrocardiogram (ECG) signals. The results showed that our method was able to accurately predict the onset of SCA with an accuracy of 96% approximately 90 minutes before it occurred. Predictions can save many lives. That is, optimized deep learning models can outperform manual models in analyzing long-term signals.
PERFORMANCE EVALUATION OF ARTIFICIAL NEURAL NETWORKS FOR CARDIAC ARRHYTHMIA C...IAEME Publication
In this paper an effective and most reliable method for appropriate classification of cardiac arrhythmia using automatic Artificial Neural Network (ANN) has been proposed. The results are encouraging and are found to have produced a very confident and efficient arrhythmia classification, which is easily applicable in diagnostic decision support system. The authors have employed 3 neural network classifiers to classify three types of beats of ECG signal, namely Normal (N), and two abnormal beats Right Bundle Branch Block (RBBB) and Premature Ventricular Contraction (PVC). The classifiers used in this paper are K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC) and Multi-Class Support Vector Machine (MSVM). The performance of the classifiers is evaluated using 5 parametric measures namely Sensitivity (Se), Specificity (Sp), Precision (Pr), Bit Error Rate (BER) and Accuracy (A). Hence MSVM classifier using Crammers method is very effective for proper ECG beat classification.
This research task develops a mobile healthcare analysis system (PHAS) which combines both easy ECG signal measurement and reliable analysis of heart rate variability for home care purpose. The PHAS is composed by a health care platform (HCP) and a data system analysis (DSA) module. The HCP consists of a self-developed two pole electrocardiography (ECG) measuring device and the DSA a data processing unit for detection and analysis of heart rate variability. For the DSA module, the adaptive R Peak detection algorithm is proposed to reliably detect the R peak of ECG for HRV analysis. A number of features are extracted from ECG signals. A data mining method is employed for HRV analysis to exploit the correlation between HRV and these features. Experiments are conducted by establishing a database of ECG signals measured from 29 subjects under rest and exercise condition. The results show the PHAS’s significant potential in mobile applications of personal daily health care.
Classifying electrocardiograph waveforms using trained deep learning neural n...IAESIJAI
Due to the rise in cardiac patients, an automated system that can identify different heart disorders has been created to lighten and distribute the duty of physicians. This research uses three different electrocardiograph (ECG) signals as indicators of a person's cardiac problems: Normal sinus rhythm (NSR), arrhythmia (ARR), and congestive heart failure (CHF). The continuous wavelet transform (CWT) provides the mechanism for classifying the 190 individual cases of ECG data into a 2-dimensional time-frequency representation. In this paper, the modified GoogLeNet is used for ECG data classification. Using a transfer learning approach and adjustments to parts of the output layers, ECG classification was conducted and the effectiveness of convolutional neural network (CNN) designs was tested. By comparing the results that the optimized neural network and GoogLeNet both had classification accuracy about of 80% and 100%, respectively. The GoogLeNet provide the best result in term of accuracy and training time.
The Fusion of HRV and EMG Signals for Automatic Gender Recognition during Ste...TELKOMNIKA JOURNAL
In this paper, a new gender recognition approach in accordance with the fusion of features extracted from electromyogram (EMG) and heart rate variability (HRV) during stepping activity using a stair stepper device is proposed. The fusion of EMG and HRV is investigated based on feature fusion approach. The feature fusion is carried out by chaining the feature vector extracted from the EMG and HRV signals. A proposed approach comprises of a sequence of processing steps which are preprocessing, feature extraction, feature selection and the feature fusion. The results demonstrated that the fusion approach had enhanced the performance of gender recognition compared to solely on EMG or HRV for the gender recognition.
Submission Deadline: 30th September 2022
Acceptance Notification: Within Three Days’ time period
Online Publication: Within 24 Hrs. time Period
Expected Date of Dispatch of Printed Journal: 5th October 2022
MODELING AND ANALYSIS OF SURFACE ROUGHNESS AND WHITE LATER THICKNESS IN WIRE-...IAEME Publication
White layer thickness (WLT) formed and surface roughness in wire electric discharge turning (WEDT) of tungsten carbide composite has been made to model through response surface methodology (RSM). A Taguchi’s standard Design of experiments involving five input variables with three levels has been employed to establish a mathematical model between input parameters and responses. Percentage of cobalt content, spindle speed, Pulse on-time, wire feed and pulse off-time were changed during the experimental tests based on the Taguchi’s orthogonal array L27 (3^13). Analysis of variance (ANOVA) revealed that the mathematical models obtained can adequately describe performance within the parameters of the factors considered. There was a good agreement between the experimental and predicted values in this study.
A STUDY ON THE REASONS FOR TRANSGENDER TO BECOME ENTREPRENEURSIAEME Publication
The study explores the reasons for a transgender to become entrepreneurs. In this study transgender entrepreneur was taken as independent variable and reasons to become as dependent variable. Data were collected through a structured questionnaire containing a five point Likert Scale. The study examined the data of 30 transgender entrepreneurs in Salem Municipal Corporation of Tamil Nadu State, India. Simple Random sampling technique was used. Garrett Ranking Technique (Percentile Position, Mean Scores) was used as the analysis for the present study to identify the top 13 stimulus factors for establishment of trans entrepreneurial venture. Economic advancement of a nation is governed upon the upshot of a resolute entrepreneurial doings. The conception of entrepreneurship has stretched and materialized to the socially deflated uncharted sections of transgender community. Presently transgenders have smashed their stereotypes and are making recent headlines of achievements in various fields of our Indian society. The trans-community is gradually being observed in a new light and has been trying to achieve prospective growth in entrepreneurship. The findings of the research revealed that the optimistic changes are taking place to change affirmative societal outlook of the transgender for entrepreneurial ventureship. It also laid emphasis on other transgenders to renovate their traditional living. The paper also highlights that legislators, supervisory body should endorse an impartial canons and reforms in Tamil Nadu Transgender Welfare Board Association.
BROAD UNEXPOSED SKILLS OF TRANSGENDER ENTREPRENEURSIAEME Publication
Since ages gender difference is always a debatable theme whether caused by nature, evolution or environment. The birth of a transgender is dreadful not only for the child but also for their parents. The pain of living in the wrong physique and treated as second class victimized citizen is outrageous and fully harboured with vicious baseless negative scruples. For so long, social exclusion had perpetuated inequality and deprivation experiencing ingrained malign stigma and besieged victims of crime or violence across their life spans. They are pushed into the murky way of life with a source of eternal disgust, bereft sexual potency and perennial fear. Although they are highly visible but very little is known about them. The common public needs to comprehend the ravaged arrogance on these insensitive souls and assist in integrating them into the mainstream by offering equal opportunity, treat with humanity and respect their dignity. Entrepreneurship in the current age is endorsing the gender fairness movement. Unstable careers and economic inadequacy had inclined one of the gender variant people called Transgender to become entrepreneurs. These tiny budding entrepreneurs resulted in economic transition by means of employment, free from the clutches of stereotype jobs, raised standard of living and handful of financial empowerment. Besides all these inhibitions, they were able to witness a platform for skill set development that ignited them to enter into entrepreneurial domain. This paper epitomizes skill sets involved in trans-entrepreneurs of Thoothukudi Municipal Corporation of Tamil Nadu State and is a groundbreaking determination to sightsee various skills incorporated and the impact on entrepreneurship.
DETERMINANTS AFFECTING THE USER'S INTENTION TO USE MOBILE BANKING APPLICATIONSIAEME Publication
The banking and financial services industries are experiencing increased technology penetration. Among them, the banking industry has made technological advancements to better serve the general populace. The economy focused on transforming the banking sector's system into a cashless, paperless, and faceless one. The researcher wants to evaluate the user's intention for utilising a mobile banking application. The study also examines the variables affecting the user's behaviour intention when selecting specific applications for financial transactions. The researcher employed a well-structured questionnaire and a descriptive study methodology to gather the respondents' primary data utilising the snowball sampling technique. The study includes variables like performance expectations, effort expectations, social impact, enabling circumstances, and perceived risk. Each of the aforementioned variables has a major impact on how users utilise mobile banking applications. The outcome will assist the service provider in comprehending the user's history with mobile banking applications.
ANALYSE THE USER PREDILECTION ON GPAY AND PHONEPE FOR DIGITAL TRANSACTIONSIAEME Publication
Technology upgradation in banking sector took the economy to view that payment mode towards online transactions using mobile applications. This system enabled connectivity between banks, Merchant and user in a convenient mode. there are various applications used for online transactions such as Google pay, Paytm, freecharge, mobikiwi, oxygen, phonepe and so on and it also includes mobile banking applications. The study aimed at evaluating the predilection of the user in adopting digital transaction. The study is descriptive in nature. The researcher used random sample techniques to collect the data. The findings reveal that mobile applications differ with the quality of service rendered by Gpay and Phonepe. The researcher suggest the Phonepe application should focus on implementing the application should be user friendly interface and Gpay on motivating the users to feel the importance of request for money and modes of payments in the application.
VOICE BASED ATM FOR VISUALLY IMPAIRED USING ARDUINOIAEME Publication
The prototype of a voice-based ATM for visually impaired using Arduino is to help people who are blind. This uses RFID cards which contain users fingerprint encrypted on it and interacts with the users through voice commands. ATM operates when sensor detects the presence of one person in the cabin. After scanning the RFID card, it will ask to select the mode like –normal or blind. User can select the respective mode through voice input, if blind mode is selected the balance check or cash withdraw can be done through voice input. Normal mode procedure is same as the existing ATM.
IMPACT OF EMOTIONAL INTELLIGENCE ON HUMAN RESOURCE MANAGEMENT PRACTICES AMONG...IAEME Publication
There is increasing acceptability of emotional intelligence as a major factor in personality assessment and effective human resource management. Emotional intelligence as the ability to build capacity, empathize, co-operate, motivate and develop others cannot be divorced from both effective performance and human resource management systems. The human person is crucial in defining organizational leadership and fortunes in terms of challenges and opportunities and walking across both multinational and bilateral relationships. The growing complexity of the business world requires a great deal of self-confidence, integrity, communication, conflict and diversity management to keep the global enterprise within the paths of productivity and sustainability. Using the exploratory research design and 255 participants the result of this original study indicates strong positive correlation between emotional intelligence and effective human resource management. The paper offers suggestions on further studies between emotional intelligence and human capital development and recommends for conflict management as an integral part of effective human resource management.
VISUALISING AGING PARENTS & THEIR CLOSE CARERS LIFE JOURNEY IN AGING ECONOMYIAEME Publication
Our life journey, in general, is closely defined by the way we understand the meaning of why we coexist and deal with its challenges. As we develop the "inspiration economy", we could say that nearly all of the challenges we have faced are opportunities that help us to discover the rest of our journey. In this note paper, we explore how being faced with the opportunity of being a close carer for an aging parent with dementia brought intangible discoveries that changed our insight of the meaning of the rest of our life journey.
A STUDY ON THE IMPACT OF ORGANIZATIONAL CULTURE ON THE EFFECTIVENESS OF PERFO...IAEME Publication
The main objective of this study is to analyze the impact of aspects of Organizational Culture on the Effectiveness of the Performance Management System (PMS) in the Health Care Organization at Thanjavur. Organizational Culture and PMS play a crucial role in present-day organizations in achieving their objectives. PMS needs employees’ cooperation to achieve its intended objectives. Employees' cooperation depends upon the organization’s culture. The present study uses exploratory research to examine the relationship between the Organization's culture and the Effectiveness of the Performance Management System. The study uses a Structured Questionnaire to collect the primary data. For this study, Thirty-six non-clinical employees were selected from twelve randomly selected Health Care organizations at Thanjavur. Thirty-two fully completed questionnaires were received.
Living in 21st century in itself reminds all of us the necessity of police and its administration. As more and more we are entering into the modern society and culture, the more we require the services of the so called ‘Khaki Worthy’ men i.e., the police personnel. Whether we talk of Indian police or the other nation’s police, they all have the same recognition as they have in India. But as already mentioned, their services and requirements are different after the like 26th November, 2008 incidents, where they without saving their own lives has sacrificed themselves without any hitch and without caring about their respective family members and wards. In other words, they are like our heroes and mentors who can guide us from the darkness of fear, militancy, corruption and other dark sides of life and so on. Now the question arises, if Gandhi would have been alive today, what would have been his reaction/opinion to the police and its functioning? Would he have some thing different in his mind now what he had been in his mind before the partition or would he be going to start some Satyagraha in the form of some improvement in the functioning of the police administration? Really these questions or rather night mares can come to any one’s mind, when there is too much confusion is prevailing in our minds, when there is too much corruption in the society and when the polices working is also in the questioning because of one or the other case throughout the India. It is matter of great concern that we have to thing over our administration and our practical approach because the police personals are also like us, they are part and parcel of our society and among one of us, so why we all are pin pointing towards them.
A STUDY ON TALENT MANAGEMENT AND ITS IMPACT ON EMPLOYEE RETENTION IN SELECTED...IAEME Publication
The goal of this study was to see how talent management affected employee retention in the selected IT organizations in Chennai. The fundamental issue was the difficulty to attract, hire, and retain talented personnel who perform well and the gap between supply and demand of talent acquisition and retaining them within the firms. The study's main goals were to determine the impact of talent management on employee retention in IT companies in Chennai, investigate talent management strategies that IT companies could use to improve talent acquisition, performance management, career planning and formulate retention strategies that the IT firms could use. The respondents were given a structured close-ended questionnaire with the 5 Point Likert Scale as part of the study's quantitative research design. The target population consisted of 289 IT professionals. The questionnaires were distributed and collected by the researcher directly. The Statistical Package for Social Sciences (SPSS) was used to collect and analyse the questionnaire responses. Hypotheses that were formulated for the various areas of the study were tested using a variety of statistical tests. The key findings of the study suggested that talent management had an impact on employee retention. The studies also found that there is a clear link between the implementation of talent management and retention measures. Management should provide enough training and development for employees, clarify job responsibilities, provide adequate remuneration packages, and recognise employees for exceptional performance.
ATTRITION IN THE IT INDUSTRY DURING COVID-19 PANDEMIC: LINKING EMOTIONAL INTE...IAEME Publication
Globally, Millions of dollars were spent by the organizations for employing skilled Information Technology (IT) professionals. It is costly to replace unskilled employees with IT professionals possessing technical skills and competencies that aid in interconnecting the business processes. The organization’s employment tactics were forced to alter by globalization along with technological innovations as they consistently diminish to remain lean, outsource to concentrate on core competencies along with restructuring/reallocate personnel to gather efficiency. As other jobs, organizations or professions have become reasonably more appropriate in a shifting employment landscape, the above alterations trigger both involuntary as well as voluntary turnover. The employee view on jobs is also afflicted by the COVID-19 pandemic along with the employee-driven labour market. So, having effective strategies is necessary to tackle the withdrawal rate of employees. By associating Emotional Intelligence (EI) along with Talent Management (TM) in the IT industry, the rise in attrition rate was analyzed in this study. Only 303 respondents were collected out of 350 participants to whom questionnaires were distributed. From the employees of IT organizations located in Bangalore (India), the data were congregated. A simple random sampling methodology was employed to congregate data as of the respondents. Generating the hypothesis along with testing is eventuated. The effect of EI and TM along with regression analysis between TM and EI was analyzed. The outcomes indicated that employee and Organizational Performance (OP) were elevated by effective EI along with TM.
INFLUENCE OF TALENT MANAGEMENT PRACTICES ON ORGANIZATIONAL PERFORMANCE A STUD...IAEME Publication
By implementing talent management strategy, organizations would have the option to retain their skilled professionals while additionally working on their overall performance. It is the course of appropriately utilizing the ideal individuals, setting them up for future top positions, exploring and dealing with their performance, and holding them back from leaving the organization. It is employee performance that determines the success of every organization. The firm quickly obtains an upper hand over its rivals in the event that its employees having particular skills that cannot be duplicated by the competitors. Thus, firms are centred on creating successful talent management practices and processes to deal with the unique human resources. Firms are additionally endeavouring to keep their top/key staff since on the off chance that they leave; the whole store of information leaves the firm's hands. The study's objective was to determine the impact of talent management on organizational performance among the selected IT organizations in Chennai. The study recommends that talent management limitedly affects performance. On the off chance that this talent is appropriately management and implemented properly, organizations might benefit as much as possible from their maintained assets to support development and productivity, both monetarily and non-monetarily.
A STUDY OF VARIOUS TYPES OF LOANS OF SELECTED PUBLIC AND PRIVATE SECTOR BANKS...IAEME Publication
Banking regulations act of India, 1949 defines banking as “acceptance of deposits for the purpose of lending or investment from the public, repayment on demand or otherwise and withdrawable through cheques, drafts order or otherwise”, the major participants of the Indian financial system are commercial banks, the financial institution encompassing term lending institutions. Investments institutions, specialized financial institution and the state level development banks, non banking financial companies (NBFC) and other market intermediaries such has the stock brokers and money lenders are among the oldest of the certain variants of NBFC and the oldest market participants. The asset quality of banks is one of the most important indicators of their financial health. The Indian banking sector has been facing severe problems of increasing Non- Performing Assets (NPAs). The NPAs growth directly and indirectly affects the quality of assets and profitability of banks. It also shows the efficiency of banks credit risk management and the recovery effectiveness. NPA do not generate any income, whereas, the bank is required to make provisions for such as assets that why is a double edge weapon. This paper outlines the concept of quality of bank loans of different types like Housing, Agriculture and MSME loans in state Haryana of selected public and private sector banks. This study is highlighting problems associated with the role of commercial bank in financing Small and Medium Scale Enterprises (SME). The overall objective of the research was to assess the effect of the financing provisions existing for the setting up and operations of MSMEs in the country and to generate recommendations for more robust financing mechanisms for successful operation of the MSMEs, in turn understanding the impact of MSME loans on financial institutions due to NPA. There are many research conducted on the topic of Non- Performing Assets (NPA) Management, concerning particular bank, comparative study of public and private banks etc. In this paper the researcher is considering the aggregate data of selected public sector and private sector banks and attempts to compare the NPA of Housing, Agriculture and MSME loans in state Haryana of public and private sector banks. The tools used in the study are average and Anova test and variance. The findings reveal that NPA is common problem for both public and private sector banks and is associated with all types of loans either that is housing loans, agriculture loans and loans to SMES. NPAs of both public and private sector banks show the increasing trend. In 2010-11 GNPA of public and private sector were at same level it was 2% but after 2010-11 it increased in many fold and at present there is GNPA in some more than 15%. It shows the dark area of Indian banking sector.
EXPERIMENTAL STUDY OF MECHANICAL AND TRIBOLOGICAL RELATION OF NYLON/BaSO4 POL...IAEME Publication
An experiment conducted in this study found that BaSO4 changed Nylon 6's mechanical properties. By changing the weight ratios, BaSO4 was used to make Nylon 6. This Researcher looked into how hard Nylon-6/BaSO4 composites are and how well they wear. Experiments were done based on Taguchi design L9. Nylon-6/BaSO4 composites can be tested for their hardness number using a Rockwell hardness testing apparatus. On Nylon/BaSO4, the wear behavior was measured by a wear monitor, pinon-disc friction by varying reinforcement, sliding speed, and sliding distance, and the microstructure of the crack surfaces was observed by SEM. This study provides significant contributions to ultimate strength by increasing BaSO4 content up to 16% in the composites, and sliding speed contributes 72.45% to the wear rate
ROLE OF SOCIAL ENTREPRENEURSHIP IN RURAL DEVELOPMENT OF INDIA - PROBLEMS AND ...IAEME Publication
The majority of the population in India lives in villages. The village is the back bone of the country. Village or rural industries play an important role in the national economy, particularly in the rural development. Developing the rural economy is one of the key indicators towards a country’s success. Whether it be the need to look after the welfare of the farmers or invest in rural infrastructure, Governments have to ensure that rural development isn’t compromised. The economic development of our country largely depends on the progress of rural areas and the standard of living of rural masses. Village or rural industries play an important role in the national economy, particularly in the rural development. Rural entrepreneurship is based on stimulating local entrepreneurial talent and the subsequent growth of indigenous enterprises. It recognizes opportunity in the rural areas and accelerates a unique blend of resources either inside or outside of agriculture. Rural entrepreneurship brings an economic value to the rural sector by creating new methods of production, new markets, new products and generate employment opportunities thereby ensuring continuous rural development. Social Entrepreneurship has the direct and primary objective of serving the society along with the earning profits. So, social entrepreneurship is different from the economic entrepreneurship as its basic objective is not to earn profits but for providing innovative solutions to meet the society needs which are not taken care by majority of the entrepreneurs as they are in the business for profit making as a sole objective. So, the Social Entrepreneurs have the huge growth potential particularly in the developing countries like India where we have huge societal disparities in terms of the financial positions of the population. Still 22 percent of the Indian population is below the poverty line and also there is disparity among the rural & urban population in terms of families living under BPL. 25.7 percent of the rural population & 13.7 percent of the urban population is under BPL which clearly shows the disparity of the poor people in the rural and urban areas. The need to develop social entrepreneurship in agriculture is dictated by a large number of social problems. Such problems include low living standards, unemployment, and social tension. The reasons that led to the emergence of the practice of social entrepreneurship are the above factors. The research problem lays upon disclosing the importance of role of social entrepreneurship in rural development of India. The paper the tendencies of social entrepreneurship in India, to present successful examples of such business for providing recommendations how to improve situation in rural areas in terms of social entrepreneurship development. Indian government has made some steps towards development of social enterprises, social entrepreneurship, and social in- novation, but a lot remains to be improved.
OPTIMAL RECONFIGURATION OF POWER DISTRIBUTION RADIAL NETWORK USING HYBRID MET...IAEME Publication
Distribution system is a critical link between the electric power distributor and the consumers. Most of the distribution networks commonly used by the electric utility is the radial distribution network. However in this type of network, it has technical issues such as enormous power losses which affect the quality of the supply. Nowadays, the introduction of Distributed Generation (DG) units in the system help improve and support the voltage profile of the network as well as the performance of the system components through power loss mitigation. In this study network reconfiguration was done using two meta-heuristic algorithms Particle Swarm Optimization and Gravitational Search Algorithm (PSO-GSA) to enhance power quality and voltage profile in the system when simultaneously applied with the DG units. Backward/Forward Sweep Method was used in the load flow analysis and simulated using the MATLAB program. Five cases were considered in the Reconfiguration based on the contribution of DG units. The proposed method was tested using IEEE 33 bus system. Based on the results, there was a voltage profile improvement in the system from 0.9038 p.u. to 0.9594 p.u.. The integration of DG in the network also reduced power losses from 210.98 kW to 69.3963 kW. Simulated results are drawn to show the performance of each case.
APPLICATION OF FRUGAL APPROACH FOR PRODUCTIVITY IMPROVEMENT - A CASE STUDY OF...IAEME Publication
Manufacturing industries have witnessed an outburst in productivity. For productivity improvement manufacturing industries are taking various initiatives by using lean tools and techniques. However, in different manufacturing industries, frugal approach is applied in product design and services as a tool for improvement. Frugal approach contributed to prove less is more and seems indirectly contributing to improve productivity. Hence, there is need to understand status of frugal approach application in manufacturing industries. All manufacturing industries are trying hard and putting continuous efforts for competitive existence. For productivity improvements, manufacturing industries are coming up with different effective and efficient solutions in manufacturing processes and operations. To overcome current challenges, manufacturing industries have started using frugal approach in product design and services. For this study, methodology adopted with both primary and secondary sources of data. For primary source interview and observation technique is used and for secondary source review has done based on available literatures in website, printed magazines, manual etc. An attempt has made for understanding application of frugal approach with the study of manufacturing industry project. Manufacturing industry selected for this project study is Mahindra and Mahindra Ltd. This paper will help researcher to find the connections between the two concepts productivity improvement and frugal approach. This paper will help to understand significance of frugal approach for productivity improvement in manufacturing industry. This will also help to understand current scenario of frugal approach in manufacturing industry. In manufacturing industries various process are involved to deliver the final product. In the process of converting input in to output through manufacturing process productivity plays very critical role. Hence this study will help to evolve status of frugal approach in productivity improvement programme. The notion of frugal can be viewed as an approach towards productivity improvement in manufacturing industries.
A MULTIPLE – CHANNEL QUEUING MODELS ON FUZZY ENVIRONMENTIAEME Publication
In this paper, we investigated a queuing model of fuzzy environment-based a multiple channel queuing model (M/M/C) ( /FCFS) and study its performance under realistic conditions. It applies a nonagonal fuzzy number to analyse the relevant performance of a multiple channel queuing model (M/M/C) ( /FCFS). Based on the sub interval average ranking method for nonagonal fuzzy number, we convert fuzzy number to crisp one. Numerical results reveal that the efficiency of this method. Intuitively, the fuzzy environment adapts well to a multiple channel queuing models (M/M/C) ( /FCFS) are very well.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.