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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Indonesian Journal of Electrical Engineering and Computer Science
Manickam, Prabhakar
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Hybrid convolutional neural network-long short-term memory combined model for arrhythmia classification Badiger, Raghavendra; Manickam, Prabhakar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4204-4213

Abstract

The automated examination of electrocardiogram (ECG) signals holds significant importance within the medical field for managing various critical cardiac conditions. Identifying cardiomyopathy and arrhythmias is presently recognized as a challenging endeavor. While machine learning techniques have garnered substantial attention for categorizing these patterns, a predominant focus has been on the classification of arrhythmias. However, existing studies have overlooked instances where arrhythmia leads to cardiomyopathy, a specific cardiac disease scenario. In our research, we introduce an innovative method aimed at distinguishing between cardiomyopathy and cardiomyopathy accompanied by arrhythmia by employing a convolutional neural network (CNN-based) model. This novel approach fills the gap in existing literature by addressing the critical need to classify cases where arrhythmia induces cardiomyopathy, thereby presenting a potential advancement in accurately identifying and managing complex cardiac conditions. The proposed model uses convolution-based CNN model for feature extraction and combines these features with temporal features. Further, a CNN combined long short-term memory (CNN-LSTM) model is presented for classification where CNN models help to obtain the spatial information and LSTM helps to retain the temporal information resulting in improved classification accuracy. the experimental analysis is carried out into two phases where we have classified the rhythms and arrhythmias.
Combined wavelet transforms and neural network feed-forward model for ECG peak detection and classification Badiger, Raghavendra; Manickam, Prabhakar
Indonesian Journal of Electrical Engineering and Computer Science Vol 35, No 2: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v35.i2.pp1343-1360

Abstract

We have focused on development of a combined approach for electrocardiogram (ECG) signal filtering and various ECG peak detection. The filtering model is based on the combination of wavelet transform and neural network where after computing the wavelet coefficients the neural network feed-forward model is used to update the weights. The filtered signal is processed through the convolution layers and bidirectional long short-term memory (Bi-LSTM) architecture to perform the ECG peak detection. Further, we apply a combined feature extraction strategy where wavelet transform and morphological feature are extracted to classify the ECG beats as classify 5 different types of heartbeats, including premature ventricular contraction (PVC), left bundle branch block (LBBB), right bundle branch block (RBBB), PACE, and atrial premature contraction (APC) to examine the heart condition. The feature extraction phase uses wavelet transform, morphological features and high-order statistics to generate the robust features. The obtained feature vector is processed through the principal component analysis (PCA) module to reduce the dimension of feature vector. These features are trained by using support vector machine (SVM) and k-nearest neighbor (KNN) supervised model. The proposed approach is tested on publicly available MIT-BIH dataset where performance analysis shows that the proposed approach obtained average precision, sensitivity and error as 99.98%, 99.96%, and 0.101 which outperforms the existing filtering and peak detection schemes.
SQL-CB-GuArd: a deep learning mechanism for structured query language injection attack detection Sirmulla, AsifIqbal; Manickam, Prabhakar
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp337-349

Abstract

Structured query language (SQL) injection attacks, which take advantage of input field vulnerabilities to introduce malicious code into database queries, are a serious danger to database-driven programs and systems. Intruders can now alter, recover, or remove sensitive data because of illegal access. Strong artificial intelligence (AI) based security solutions are required to reduce SQL injection threats, as these assaults' significance highlights. This study's main goal is to create automated AI-based techniques that can identify structured query language injection attack (SQLIA) in real time eliminating the need for human intervention. Although machine learning (ML) and deep learning-based techniques have received a lot of interest in this field, MLbased techniques have problems with accuracy and false negatives. Deep learning (DL) is therefore commonly used in these text data processing and natural language processing (NLP) applications. We have introduced a hybrid DL approach for SQLIA detection in this paper. The pre-processing step performs decoding, generalization, and tokenization to improve the learning performance. The proposed approach uses combination of convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), gated recurrent unit (GRU) with attention mechanism. The combination helps to improve the pattern learning capacity. The proposed approach is validated on publically available data and experimental analysis reported that the proposed SQL-CB-GuArd achieves better accuracy of SQLIA detection.
Co-Authors Badiger, Raghavendra Sirmulla, AsifIqbal