Aplikasi dan Kerentanan Algoritma Probabilistic Neural Network (PNN): Systematic Literature Review

PNN (Probabilistic Neural Network) adalah salah satu jenis jaringan saraf tiruan (artificial neural network) yang dapat digunakan untuk berbagai macam aplikasi, seperti prediction, classification, word embedding, medical detection, biometric identification dan aplikasi lainnya. Meskipun PNN menunjukkan kinerja yang baik dalam banyak kasus, algoritma ini juga memiliki kerentanan terhadap serangan dan kekurangan tertentu. Oleh karena itu, penelitian tentang aplikasi dan kerentanan PNN sangat penting dalam pengembangan sistem pembelajaran mesin yang lebih aman dan andal. Penelitian ini bertujuan untuk melakukan tinjauan literatur sistematis tentang aplikasi dan kerentanan PNN. Metode tinjauan literatur sistematis digunakan untuk mengidentifikasi dan menganalisis publikasi terkait PNN dari berbagai sumber seperti jurnal ilmiah. Hasil tinjauan literatur ini menunjukkan bahwa PNN telah berhasil digunakan dalam berbagai aplikasi dan menunjukkan kinerja yang baik. Namun, beberapa studi juga mengungkapkan kerentanan dan kelemahan PNN. Penelitian ini memberikan wawasan tentang aplikasi dan kerentanan PNN, yang dapat digunakan sebagai dasar untuk pengembangan teknik yang lebih aman dan andal dalam pembelajaran mesin. Hasil tinjauan literatur ini juga dapat digunakan sebagai sumber referensi bagi peneliti yang tertarik dalam pengembangan sistem pembelajaran mesin yang lebih baik dan andal menggunakan algoritma PNN.PNN (Probabilistic Neural Network) is an artificial neural network that can be used for various applications, such as prediction, classification, word embedding, medical detection, biometric identification, and other applications. Although PNN performs well in most cases, this algorithm also has specific weaknesses to attacks and flaws. Therefore, research on PNN applications and vulnerabilities is fundamental in developing more secure and reliable machine learning systems. This study aims to conduct a systematic literature review on PNN applications and vulnerabilities. The systematic literature review method identifies and analyzes PNN-related publications from various sources, such as scientific journals. The results of this literature review indicate that PNN has been successfully used in various applications and shows good performance. However, several studies have also revealed the vulnerabilities and weaknesses of PNN. This research provides insight into PNN applications and vulnerabilities, which can be used to develop more secure and reliable techniques in machine learning. The results of this literature review can also be used as a reference source for researchers interested in developing better and more reliable machine learning systems using the PNN algorithm

Three-dimensional Phase Space Characteristics of Electrocardiogram Segments in Online and Early Prediction of Sudden Cardiac Death

Introduction: Predicting sudden cardiac death (SCD) using electrocardiogram (ECG) signals has come to the attention of researchers in recent years. One of the most common SCD identifiers is ventricular fibrillation (VF). The main objective of the present study was to provide an online prediction system of SCD using innovative ECG measures 10 minutes before VF onset. Additionally, it aimed to evaluate the different segments of the ECG signal (which depend on ventricular function) comparatively to determine the efficient component in predicting SCD. The ECG segments were QS, RT, QR, QT, and ST.Material and Methods: After defining the ECG characteristic points and segments, innovative measures were appraised using the three-dimensional phase space of the ECG component. Tracking signal dynamics and lowering the computational cost make the feature suitable for online and offline applications. Finally, the prediction was performed using the support vector machine (SVM).Results: Using the QR measures, SCD detection was realized ten minutes before its occurrence with an accuracy, specificity, and sensitivity of 100%.Conclusion: The superiority of the proposed system compared to the state-of-the-art SCD prediction schemes was revealed in terms of both classification performances and computational speed