Biometric-Based Human Identification Using Ensemble-Based Technique and ECG Signals

User authentication has become necessary in different life domains. Traditional authentication methods like personal information numbers (PINs), password ID cards, and tokens are vulnerable to attacks. For secure authentication, methods like biometrics have been developed in the past. Biometric information is hard to lose, forget, duplicate, or share because it is a part of the human body. Many authentication methods focused on electrocardiogram (ECG) signals have achieved great success. In this paper, we have developed cardiac biometrics for human identification using a deep learning (DL) approach. Cardiac biometric systems rely on cardiac signals that are captured using the electrocardiogram (ECG), photoplethysmogram (PPG), and phonocardiogram (PCG). This study utilizes the ECG as a biometric modality because ECG signals are a superior choice for accurate, secure, and reliable biometric-based human identification systems, setting them apart from PPG and PCG approaches. To get better performance in terms of accuracy and computational time, we have developed an ensemble approach based on VGG16 pre-trained transfer learning (TL) and Long Short-Term Memory (LSTM) architectures to optimize features. To develop this authentication system, we have fine-tuned this ensemble network. In the first phase, we preprocessed the ECG biosignal to remove noise. In the second phase, we converted the 1-D ECG signals into a 2-D spectrogram image using a transformation phase. Next, the feature extraction step is performed on spectrogram images using the proposed ensemble DL technique, and finally, those features are identified by the boosting machine learning classifier to recognize humans. Several experiments were performed on the selected dataset, and on average, the proposed system achieved 98.7% accuracy, 98.01% precision, 97.1% recall, and 0.98 AUC. In this paper, we have compared the developed approach with state-of-the-art biometric authentication systems. The experimental results demonstrate that our proposed system outperformed the human recognition competition

Network Decontamination

The Network Decontamination problem consists in coordinating a team of mobile agents in order to clean a contaminated network. The problem is actually equivalent to tracking and capturing an invisible and arbitrarily fast fugitive. This problem has natural applications in network security in computer science or in robotics for search or pursuit-evasion missions. In this Chapter, we focus on networks modeled by graphs. Many different objectives have been studied in this context, the main one being the minimization of the number of mobile agents necessary to clean a contaminated network. Another important aspect is that this optimization problem has a deep graph-theoretical interpretation. Network decontamination and, more precisely, graph searching models, provide nice algorithmic interpretations of fundamental concepts in the Graph Minors theory by Robertson and Seymour. For all these reasons, graph searching variants have been widely studied since their introduction by Breish (1967) and mathematical formaliza-tions by Parsons (1978) and Petrov (1982). This chapter consists of an overview of algorithmic results on graph de-contamination and graph searching