Power Optimization of I/O Ports Using Clock Gating TechniqueÂ

No Abstrac

Recognition and Evaluation of Heart Arrhythmias via a General Sparse Neural Network

In clinical use, an electrocardiogram (ECG) is an essential medical tool for assessing heart arrhythmias. Thousands of human beings worldwide are affected by different cardiac problems nowadays. As a consequence, studying the features of the ECG pattern is critical for detecting a wide range of cardiac diseases. The ECG is a test which assesses the intensity of the electrical impulses in the circulatory system. In the present investigation, detection and examination of arrhythmias in the heart on the  system using GSNNs (General sparsed neural network classifier) can be carried out[1]. In this paper, the methodologies of support vector regression(SVR), neural mode decomposition(NMD), Artificial Neural Network (ANN), Support Vector Machine(SVM) and are examined. To assess the suggested structure, three distinct ECG waveform situations are chosen from the MIT-BIH arrhythmia collection. The main objective of this assignment is to create a simple, accurate, and simply adaptable approach for classifying the three distinct heart diseases chosen. The wavelet transform Db4 is used in the present paper to obtain several features from an ECG signal. The suggested setup was created using the MATLAB programme. The algorithms suggested are 98% accurate for forecasting cardiac arrhythmias, which is greater than prior techniques

Feature Classification and Extreme Learning Machine Based Detection of Phishing Websites

Phishing is a cyber-attack that uses a phishing website impersonating a real website to deceive internet users into disclosing sensitive information. Attackers using stolen credentials not only utilize them for the targeted website, but they may also be used to access other famous genuine websites. This paper proposes a novel approach for detecting phishing websites using a feature classification technique and an Extreme Learning Machine (ELM) algorithm. The proposed system extracts various features from the website URL and content, including text-based, image-based, and behavior-based features. These features are then classified using a feature selection technique, which selects the most relevant features to improve the detection accuracy. The selected features are then fed into the ELM algorithm, which is a powerful machine learning method for classifying and predicting data. The ELM algorithm It trains upon a huge set of data legitimate & phishing websites, and final outcome model is applied to classify unknown websites as either legitimate or phishing. The proposed approach is evaluated on several benchmark datasets and compared with other state-of-the-art phishing detection methods. The experimental results demonstrate that the proposed approach achieves high detection accuracy and outperforms other methods in terms of precision, recall, and F1-score. The proposed approach can be used as an effective tool for detecting and preventing phishing attacks, which are a major threat to the security of online users